• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.675-681
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• 2021

Recently, the Ministry of Education stipulates in the distance class operation regulations that student lecture evaluations for distance learning subjects should be conducted at least twice per semester and the results should be disclosed to students. Therefore, the lecture evaluation of D college was compared with the first semester of 2020 and the first semester of 2021. As for the multiple-choice evaluation result of the distance learning mid-course evaluation, the overall average of the mid-course evaluation of the distance class in the first semester of 2020 increased from 4.1819 to 4.4000 in the mid-course evaluation in the first semester of 2021.In the case of the first semester of 2020, due to Corona 19, all non-face-to-face classes were held, but in the first semester of 2021, face-to-face classes increased. The overall satisfaction level rose from 4.18 points in the first semester of 2020 to 4.39 points in the first semester of 2021. The screen composition, sound and picture quality, playback time, face appearance, lecture material provision, and frequency of use of the top 3% and bottom 3% also increased. Despite the changes caused by the LMS replacement, which was a concern, student attendance, assignments, and test submission rates also increased compared to the previous year. The null hypothesis that 'the difference between the two scores is the same' is the null hypothesis because the probability of significance is 0.000 and less than 0.05 in the case of the best 3% of the test result of the test result of the mid-course evaluation of distance classes in the first semester of 2020 and the evaluation of the intermediate lectures in the first semester of 2021. As this was rejected, it can be seen that the best score for the 2021 school year has significantly increased compared to the first semester of 2020. Also, in the case of Worst 3% or less, the significance probability is 0.000, which is less than 0.05, so the null hypothesis that 'the difference between the two scores is the same' was rejected, indicating that the Worst score for the 2021 school year was significantly higher than that for the first semester of 2020.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.848-857
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• 2018

Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.1
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• pp.8-17
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• 2019

Purpose: The study analyzed the prevalence of peri-implantitis and factors which may have affected the disease. Materials and methods: This study based on medical records and radiographs of 422 patients (853 implant cases) who visited Ewha Womans University Mokdong Hospital Dental Center from January 1, 2012 to December 31, 2016. Generalized estimation equations (GEE) was utilized to determine the statistical relationship between peri-implantitis and each element, and the cumulative prevalence of peri-implantitis during the observation period was obtained by using the Kaplan Meier Method. Results: The prevalence rate of peri-implantitis at the patient level resulted in 7.3% (31 patients out of a total of 422 patients), and at the implant level 5.5% (47 implants out of a total of 853 implants). Sex, GBR, guided bone regeneration (GBR) and functional loading periods had statistical significance with the occurrence of peri-implantitis. Upon analysis of the cumulative prevalence of peri-implantitis in terms of implant follow-up period, the first case of peri-implantitis occurred at 9 months after the placement of an implant, and the prevalence of peri-implantitis showed a non-linear rise over time without a hint of a critical point. Conclusion: The prevalence of peri-implantitis at the patient level and the implant were 7.3% and 5.5%, respectively. Male, implant installed with GBR and longer Functional Loading Periods were related with the risk of peri-implantitis.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.482-496
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• 2023

Many changes in the scale and structure of the Korean rice cropping system have been made over the past few decades. Still, insufficient research has been conducted on the sustainability of this system. This study analyzed changes in the Korean rice cropping system's sustainability from a system ecology perspective using an emergy approach. For this purpose, an emergy table was created for the Korean rice cropping system in 2011, 2016, and 202, and an emergy-based indicator analysis was performed. The emergy analysis showed that the total emergy input to the rice cropping system decreased from 10,744E+18 sej year^-1 to 8,342E+18 sej year^-1 due to decreases in paddy field areas from 2011 to 2021, and the proportion of renewable resources decreased by 1.4%. The emergy input per area (ha) was found to have decreased from 13.13E+15 sej ha^-1 year^-1 in 2011 to 11.89E+15 sej ha^-1 year^-1 in 2021, and the leading cause was a decrease in nitrogen fertilizer usage and working hours. The amount of emergy used to grow 1 g of rice stayed the same between 2016 and 2021 (specific emergy: 13.3E+09 sej g^-1), but the sustainability of the rice cropping system (emergy sustainability index, ESI) continued to decrease (2011: 0.107, 2016: 0.088, and 2021: 0.086). This study provides quantitative information on the emergy input structure and characteristics of Korean rice cropping systems. The results of this study can be used as a valuable reference in establishing measures to improve the ecological sustainability of the Korean rice cropping system.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.3
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• pp.15-24
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• 2022

In 2000, a total of 23,794ha of forest was lost due to the East Coast forest fire, and about 70% of the damaged area was concentrated in Samcheok. In 2001, artificial restoration and natural restoration were implemented in the damaged area. This study was conducted to understand the current vegetation structure 21 years after the restoration of forest fire damage in the Samcheok, Gumbong Mountain area. As a result of classifying the vegetation community, it was divided into three communities: Quercus variabilis-Pinus densiflora community, Pinus densiflora-Quercus mongolica community, and Pinus thunbergii community. Quercus variabilis, Pinus densiflora, and Pinus thunbergii planted in the artificial restoration site were found to continue to grow as dominant species in the local vegetation after restoration. As for the species diversity index of the community, the Quercus variabilis-Pinus densiflora community dominated by deciduous broad-leaf trees showed the highest, and the coniferous forest Pinus thunbergii community showed the lowest. Vegetation in areas affected by forest fires is greatly affected by reforestation tree species, and 21 years later, it has shown a tendency to recover to the forest type before forest fire. In order to establish DataBase for effective restoration and to prepare monitoring data, it is necessary to construct data through continuous vegetation survey on the areas affected by forest fires.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.