• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.101-127
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• 2021

The purpose of this study is to design and develop a teaching-learning process plan for process-based assessment, focusing on the unit related to life design in middle school home economics(HE: Home Economics part of 「Technology and Home Economics」), to propose a feedback plan after implementing it, and to evaluate the plan through participatory observation and interviews. The student reflection journals, teacher's class journals, participatory observation journals, interviews, and performance tasks, were collected and analyzed to provide foundational date to be utilized for feedback to students, and class improvement. The research results are as follows: First, the developed teaching-learning process plan consists of a total of 8 sessions, i.e. 2 sessions for each of the four learning themes, under the practical question of "What should I do to live the life I want?" The portfolio was composed of five evaluation topics and for evaluation, oral presentation, observational evaluation, self-assessment, and peer evaluation were considered. Second, during the class, feedback from teachers, feedback from fellow students, feedback through results, and a plan to record them were provided. Third, from the analysis of collected data including observation journals and interviews, it was apparent that the students recognized the necessity of process-based assessment after the class, and students acknowledged that through the process-based evaluation in which they are evaluated on the efforts they made and provided with feedbacks, they participated more in class, and it lead them to experience a sense of growth and a feeling that they took a step forward into their future. Teachers suggested that the class through feedback was suitable for the unit and the capacity of the class, but the difficulty they experienced in giving feedback was presented as a disadvantage. For the process-based assessment, follow-up research is needed on various ways to provide feedback on-line and off-line through changes in the perception of assessment.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.903-913
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• 2021

In the flood season, the measurement of the river discharge has many restrictions due to reasons such as budget, manpower, safety, convenience in measurement and so on. In particular, when heavy rain events occur due to typhoons, etc., it is difficult to measure the amount of flood due to the above problems. In order to improve this problem, in this study, a method was developed that can measure the river discharge in a flood season simply and safely in a short time with minimal manpower by combining the functions of a drone and a surface velocity doppler radar. To overcome the mechanical limitations of drones caused by weather issues such as wind and rainfall derived from the measurement of the river discharge using the conventional drone, we developed a drone with P56 grade dustproof and waterproof performance, stable flight capability at a wind speed of up to 36 km/h, and a payload weight of up to 10 kg. Further, to eliminate vibration which is the most important constraint factor in the measurement with a surface velocity doppler radar, a damper plate was developed as a device that combines a drone and a surface velocity Doppler radar. The velocity meter DSVM (Dron and Surface Veloctity Meter using doppler radar) that combines the flight equipment with the velocity meter was produced. The error of ±3.5% occurred as a result of measuring the river discharge using DSVM at the point of Geumsan-gun (Hwangpunggyo) located at Bonghwang stream (the first tributary stream of the Geum River). In addition, when calculating the mean velocity from the measured surface velocity, the measurement was performed using ADCP simultaneously to improve accuracy, and the mean velocity conversion factor (0.92) was calculated by comparing the mean velocity. In this study, the discharge measured by combining a drone and a surface velocity meter was compared with the discharge measured using ADCP and floats, so that the application and utility of DSVM was confirmed.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.267-272
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• 2021

This experiment was conducted to the purpose of evaluating the effect of feeding on Korean Native Cattle to expand the production and utilization of whole crop rice (WCR). TMR (Total mixed ration) feed was prepared by using WCR produced in Pyeongtaek, Gyeonggi-do, and the growth capacity and meat quality characteristics of 16 Korean Native Cattle raised up to 29 months of age were investigated. The produced WCR silage for feed had a moisture content of 64.02% and a crude protein content of 7.54%, and was blended with about 45% during the growing season, and lowered to 35, 15 and 9%, respectively, in the fattening period (early, middle and late stages). The body weight of the WCR-TMR feeding group was significantly higher than that of the control in the middle and late fattening stage, and at the end (29 months of age), the control group was 631 kg/head, but the WCR-TMR feeding group was 647 kg/head, which was higher. The average daily gain was significantly higher in the WCR-TMR feeding group in the growing and early fattening period, and there was no difference in the mid- and late fattening period. In the whole period, 0.71 vs 0.75 kg/head/day, WCR-TMR feeding group was high. In terms of meat quantity, the back fat thickness of the control group (11.7 mm) was significantly thicker than that of the WCR-TMR fed group (9.3 mm) (P<0.05). There was no difference in Rib eye area, Carcass weight and Meat yield index (P>0.05). In terms of meat quality, the Marbling score was higher in the WCR-TMR feeding group (P<0.05), and there were no significant differences in Meat color, Fat color, Texture and Maturity. Considering the above results, TMR feeding mainly on whole crop rice silage for feed improved the productivity of livestock, but there was no significant difference in meat quality. Therefore, it is judged that it is necessary to produce and use the whole crop rice for feed in countries with poor forage conditions.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Korean small business review
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• v.42 no.4
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• pp.69-102
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• 2020

Shedding new light on the research trend on entrepreneurial ecosystems in the 40-year history of the Asia Pacific Journal of Small Business, this study aims at exploring a potential measurement framework of ecological inputs and outputs in an entrepreneurial ecosystem that promotes entrepreneurship at geographical and spatial levels. As a result of the analysis of research on the entrepreneurial ecosystem in the journal, we found that prior studies emphasized the managerial importance of various ecological factors on the premise of possible causalities between the factors and entrepreneurship. However, empirical research to verify the premised causality has been underexplored yet. This literature gap may lead to unbalanced development of conceptual and case studies that identify requirements for successful entrepreneurial ecosystems based on experiential facts, thereby hindering the generalization of the research results for practical implications. In that there is a growing interest in creating and operating productive entrepreneurial ecosystems as an innovation engine that drives national and regional economic growth, it is necessary to explore and develop the measurement framework for ecological factors that can be used in future empirical research. Hereupon, we apply a conceptual model of 'input-output-outcome-impact' to categorize individual environmental factors identified in prior studies. Based on the model. We operationalize ecological input factors as the financial, intellectual, institutional, and social capitals, and ecological output factors as the establishment-based, innovation-based, and performance-based entrepreneurship. Also, we propose several longitudinal databases that future empirical research can use in analyzing the potential causality between the ecological input and output factors. The proposed framework of entrepreneurial ecosystems, which focuses on measuring ecological input and output factors, has a high application value for future research that analyzes the causality.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.4
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• pp.373-383
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• 2022

Inflammation caused by active oxygen and the resulting barrier damage have been consistently pointed out as the cause of wrinkle formation. In this study, effective index ingredient search and efficacy analysis were performed to verify the value of use as a functional cosmetic material related to antioxidant, anti-inflammatory and skin barrier improvement, and anti-aging for extracts of four types of Eleutherococcus divaricatus var. chiisanensis (ED), Eleutherococcus senticosus (EN), Eleutherococcus sessiliflorus (ES), and Eleutherococcus sieboldianus (EI) belonging to the Eleutherococcus genus. To identify the effective index composition, the content of the ingredients was measured by high-performance liquid chromatography. The content of eleutheroside E and chlorogenic acid was the highest in ED among the Eleutherococcus genus. As for anti-oxidant activity, DPPH radical scavenging activity was the highest in ED. In anti-inflammatory effects, ED extracts inhibited nitric oxide generation in inflammatory macrophage cells due to lipopolysaccharide by 40% at 100 ㎍/mL. In the case of IL-6 inhibition, which is known as a pro-inflammatory cytokine, ED showed 41% inhibition at 100 ㎍/mL. In addition, filaggrin and involucrin, which are skin barrier-related factors, were increased by 2.5 times and 1.6 times, respectively, in 100 ㎍/mL of ED extracts, and as for the collagenase, which is a wrinkle-related factor, ED extract showed 29% efficacy at 100 ㎍/mL. Thus, these result suggested that ED extract, among the four Eleutherococcus genus, can be used as a cosmetic ingredient for suppressing inflammation in the skin, reinforcing the skin barrier, and reducing wrinkles.

• Journal of Life Science
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• v.32 no.8
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• pp.601-610
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• 2022

P. tenebrifer (PT) belongs to the Diptera order and Stratiomyidae family. Recently, insect industry have been focused as food, animal feed and environmental advantages. γ-aminobutyric acid (GABA) and melatonin have been associated with regulating sleep and depression. GABA is the primary inhibitory neurotransmitter and is synthesized via biotransformation of monosodium glutamate (MSG) to GABA by lactic acid bacteria. In this study, we first used a GABA-enhanced PT extract, wherein GABA was enhanced by feeding MSG to PT. The underlying mechanisms preventing stress and insomnia were investigated in a corticosterone (CORT)-induced endoplasmic reticulum (ER) stress and chronic restraint stress (CRS)-exposed mouse model, as well as in pentobarbital (45 mg/kg)-induced sleep behaviors in mice. In the present study, the GABA peak was detected in high-performance liquid chromatography-evaporative light scattering detector (HPLC-ELSD) analysis and showed in Ptecticus tenebrifer water extract (PTW) but not in non-PTW extract. The results showed that PTW and Ptecticus tenebrifer with 70% ethanol extract (PTE) exerted neuroprotective effects by protecting against CORT-induced downregulation of phosphorylated extracellular signal-regulated kinase 1/2 (ERK1/2) and cAMP-response element binding protein (CREB) expression. In addition, PTW (300 mg/kg) significantly reduced CORT levels in CRS-exposed mice. Furthermore, PTW (100 and 300 mg/kg) significantly reduced sleep latency and increased total sleep duration in pentobarbital (45 mg/kg)-induced sleeping behaviors, which was related to serum melatonin levels. In conclusion, our results suggest that PTW exerts anti-stress and sleep-enhancing effects by regulating serum CORT and melatonin levels.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.3
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• pp.15-25
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• 2023

In this study, silicon sludge from semiconductor dicing process is recycled to fabricate silica nanoparticles, which are applied as dispersing materials for electro-responsive (ER) smart fluid. In specific, metal impurities are removed from silicon sludge by acid washing to obtain the high-purity silicon powder. And then, silica nanoparticles are synthesized by facile hydrothermal method employing the silicon powder as reactant material. To control the size of silica nanoparticles, the reaction time of hydrothermal method is varied as 8, 15, 20, and 30 hours are applied to control the size of silica nanoparticles. Sizes of silica nanoparticles are increased proportionally to the reaction time owing to the increased numbers of hydrolysis and condensation reactions. As-synthesized silica nanoparticles are prepared as electro-responsive smart fluids by dispersing into silicon oil. Silica nanoparticles synthesized by 30 hours of hydrothermal reaction (SiO₂-H30) exhibit the highest shear stress of 21.4 Pa under an applied electric field strength of 3.0kV mm^-1. Such enhancement in ER performance of SiO₂-H30 among various silica nanoparticles are attribute to the reinforcing effect originated from the mixed particle size, which allowing the formation of rigid chain-like structures. Accordingly, this study successfully propose a recycling method of silicon sludge to synthesize silica nanoparticles and their derived ER fluids, which may suggest new possibility to ESG management emphasizing the eco-friendliness.