• Particle and aerosol research
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• v.16 no.4
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• pp.107-117
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• 2020

Based on the airborne measurement results over a coal fired power plant and steel work in Dangjin city, SO2 emission amounts of each site are estimated (top-down emission). Airborne measurements were carried out on May-June and October-November 2019. The estimated SO2 emission in 2019 for the power plant was 1502.1 kg/hr and that for the steel work was 2850.5 kg/hr, higher as much as a factor of 2.5 and 2.0, respectively, than the emission amounts provided by both facilities (bottom-up emission). The outcomes strongly illustrates that well designed airborne observations can serve a quantitative diagnostic tool for bottom-up emission estimates. Further research direction to improve the reliability of the top-down emission estimates is suggested.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.402-407
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• 2011

The objectives of this study were to analyze the crown vertical structure, crown bulk density, and to develop regression models for predicting crown fuel load using the data from 10 destructively sampled Pinus densiflora trees in Bonghwa, Gyeongbuk. The fuel loads were observed higher in the middle portion of the vertical distribution of crown followed by the lower portion and upper portion of Pinus densiflora, respectively. Approximately 25% crown fuel load was found in the needle while 33% was observed in the branches with <1 cm diameter with a total of 58% available fuel loads. The average crown bulk density was $0.45kg/m^3$, and $0.27kg/m^3$ of this was available in the needles and branches with <1 cm diameters. The resulting models in linear equations were able to account for 84% and 88% of the observed variation, while the allometric equations with diameter at breast height as the single predictor showed better results to account for 90% and 95% of the observed variation in the available crown fuel loads and total crown fuel loads, respectively. The suggested equations in this study could provide quantitative fuel load attributes for crown fire behavior models and fire management of red pine stands in Bonghwa areas.

• Communications of Mathematical Education
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• v.35 no.2
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• pp.153-172
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• 2021

Various competencies such as critical thinking, systems thinking, problem solving competence, communication skill, and data literacy are likely to be required in the 4th industrial revolution. The competency regarding data literacy is one of those competencies. To nurture citizens who will live in the future, it is timely to consider research on teacher education for supporting teachers' development of statistical thinking as well as statistical knowledge. Therefore, in this study we developed and implemented a data analysis project for pre-service teachers to understand their changes in statistical knowledge in addition to their experiences of data-driven decision making process that required them utilizing their statistical thinking. We used a mixed method (i.e., sequential explanatory design) research to analyze the quantitative and qualitative data collected. The findings indicated that pre-service teachers have low knowledge level of their understanding on the relationship between population means and sample means, and estimation of the population mean and its interpretation. When it comes to the data-driven decision making process, we found that the pre-service teachers' experiences varied even when they worked as a small group for the project. We end this paper by presenting implications of the study for the fields of teacher education and statistics education.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.1
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• pp.33-37
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• 2022

Purpose ¹⁸F-Florbetaben (FBB) Readings are made by visually comparing the signal strengths of gray matter and white matter. We intend to evaluate the usefulness of image analysis by comparing quantified image analysis with readout. Materials and Methods Based on the reading results, 100 patients were divided into a negative scan and a positive scan, and 300 MBq of FBB was injected, and images were taken 90 minutes later for 20 minutes. The equipment was a Discovery 600 (GE Healthcare, MI, USA). Four regions of interest (lateral temporal lobes, frontal lobes, posterior cingulate & precuneus, and parietal lobes) were established based on the amyloid reading standard provided by the manufacturer. For image analysis, SUVratio (SUVr) was calculated by dividing each SUVmean by the cerebellum, and the average SUVr in the entire area was performed. Statistical analysis analyzed the cutoff derivation through ROC Curve, the difference between groups in Independent sample t-test, and the degree of agreement with the reading result through Kappa test. Results The average SUVr cutoff in the entire area was 1.23. Concordance with the read results using cutoff was 95/100 (95%) for negative and 92/100 (92%) for positive. As a result of the t-test, there was a statistically significant difference between the groups (P < 0.05), and the Kappa statistical result showed a high degree of agreement with 0.867 (P < 0.05). Conclusion The results of image analysis were statistically significant and showed a high degree of agreement with the reading results. In addition, FBB image analysis can be viewed by 3D mapping the area where amyloid is accumulated, location estimation is possible, and quantitative analysis results can be viewed in detail. If quantified FBB image analysis is used as an auxiliary indicator, it is thought to be helpful in reading.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.3
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• pp.303-314
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• 2009

Image analyses for sheared joint specimens are performed to study asperity degradation characteristics with respect to the roughness mobilization of rock joints. Four different types of joint specimens, which are made of high-strength gypsum materials, are prepared by replicating the three-dimensional roughness of rock joints. About twenty jointed rock shear tests are performed at various normal stress levels. The characteristic and scale of asperity degradation on the sheared joint specimens are analyzed using the digital image analysis technique. The results show that the asperity degradation characteristic mainly depends on the normal stress level and can be defined by asperity failure and wear. The asperity degradation develops significantly around the peak shear displacement and the average amount of degraded asperities remains constant with further displacement because of new degradation of small scale asperities. The shear strength results using high-strength gypsum materials can not fully represent physical properties of each mineral particles of asperities on the natural rock joint surface. However the results of this quantitative estimation for the relationship between the peak shear displacement and the asperity degradation suggest that the characterization of asperity degradation provides an important insight into mechanical characteristics and shear models of rock joints.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.11-19
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• 2008

Currently, existing car following models among several basic systems of advanced vehicle systems are almost developed related to the physical relation between two vehicles except for the driver's behavior or environmental factors. But the consideration of driver's character and environmental factors on driving are very essential factors for actual application. Hence, we suggested calibrating the degree of driver's discomfort on driving that is the former study to develop a new car following model of advanced vehicle to use in actuality. The degree of driver's discomfortness (Measure-of-Alarmness; MOA)is measured related to the relationship between the following vehicle and the preceding vehicle, the environmental factors and driver's characters in ubiquitous traffic. We made up questions to drivers to obtain the general and the objective measurement of driver's MOA. And the fuzzy logic model for measurement of MOA was constructed based on the results of survey. We verified the suitability of fuzzy logic model through the computation of MOA with several scenarios. And we measured the quantitative degree of driver's discomfortness on car following related to several factors which affect drivers. In accordance with this study, development of car following model applying driver's MOA will promote the actual application of advanced vehicle more effectively than the existing models. Finally, we thought the measurement of driver's MOA will be applied significantly to evaluate safety and comfort of drivers on driving.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.493-504
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• 2023

The Republic of Korea is located far from the boundary of the earthquake plate, and the intra-plate earthquake occurring in these areas is generally small in size and less frequent than the interplate earthquake. Nevertheless, as a result of investigating and analyzing earthquakes that occurred on the Korean Peninsula between the past two years and 1904 and earthquakes that occurred after observing recent earthquakes on the Korean Peninsula, it was found that of a magnitude of 9. In this paper, the Korean Peninsula Historical Earthquake Record (2 years to 1904) published by the National Meteorological Research Institute is used to analyze the relationship between earthquakes on the Korean Peninsula and statistical self-similarity. In addition, the problem solved through this paper was the first to investigate the relationship between earthquake data occurring on the Korean Peninsula and statistical self-similarity. As a result of measuring the degree of self-similarity of earthquakes on the Korean Peninsula using three quantitative estimation methods, the self-similarity parameter H value (0.5 < H < 1) was found to be above 0.8 on average, indicating a high degree of self-similarity. And through graph visualization, it can be easily figured out in which region earthquakes occur most often, and it is expected that it can be used in the development of a prediction system that can predict damage in the event of an earthquake in the future and minimize damage to property and people, as well as in earthquake data analysis and modeling research. Based on the findings of this study, the self-similar process is expected to help understand the patterns and statistical characteristics of seismic activities, group and classify similar seismic events, and be used for prediction of seismic activities, seismic risk assessments, and seismic engineering.