• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.305-317
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• 2002

The purpose of this study is to analyze the land cover environmental changes in the Anmyeon-do. Especially, it centers on the changes in the land cover environment through methods of GIS and remote sensing. The land cover environmental change areas were detected from remote sensing data, and geographic data sets related to land cover environment change were built as a spatial database in GIS. Fuzzy logic was applied for data representation and integration of thematic maps. In the natural, social, and economic environment variables, the altitude, population density, and the national land use planning showed higher fuzzy membership values, respectively. After integrating all thematic maps using fuzzy logic operation, it is possible to predict the change quantitatively. In the study area, a region where land cover change will be likely to occur is the one on a plain near the shoreline. In particular, the hills of less than 5% slope and less than 15m altitude, adjacent to the ocean, were quite vulnerable to the aggravation of coastal environment on account of current, large-scale development. In conclusions, it is expected that the generalized scheme used in this study is regarded as one of effective methodologies for land cover environmental change detection from geographic data.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.25-40
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• 2019

Many companies are executing big data analysis and utilization projects to legitimize the development of new business areas or conversion of management or technical strategies. In Korea and abroad, however, such projects are failing because they are not completed within specified deadlines, which is not unrelated to the current situation in which the knowledge base for big data project risk management from an engineering perspective is grossly lacking. As such, the current study analyzes the risk factors of big data implementation and utilization projects, in addition to finding risk factors that are highly important. To achieve this end, the study extracts project risk factors via literature review, after which they are grouped using affinity methodology and sifted through expert surveys. The deduced risk factors are structuralize using factor analysis to develop a table that categorizes various types of big data project risk factors. The current study is significant that in it provides a basis for developing basic control indicators related to risk identification, risk assessment, and risk analysis. The findings from the study contribute greatly to the success of big data projects, by providing theoretical basis regarding efficient big data project risk management.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.523-530
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• 2018

Kyoto Protocol, adopted in 1997, set the obligation to reduce $CO_2$, $CH_4$, $N_2O$, HFCs, PFCs, and $SF_6$ in developed countries during 1st promised period. $SF_6$ has been drawing a lot of attention since the Kyoto Protocol because once it is released into the atmosphere, it not only stays in the atmosphere for more than 3,200 years but also emits 22,800 times stronger global warming potential at the same concentrations as $CO_2$ if remains in the atmosphere for 100 years. This study introduces 12 methods for $SF_6$ of measuring trace. $SF_6$ of trace level in the atmosphere correctly, the measurement method was changed and as a result, when the back flush method was applied to the pre-concentration system that used low-temperature concentration and high-temperature desorption system, which used Carboxen-1000 adsorption trap, the effect was the best.

• Journal of Wetlands Research
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• v.23 no.3
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• pp.260-276
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• 2021

Wetlands are constantly affected by internal and external environments that make up the wetlands, and these effects make wetlands change. East Asia countries where about 15% of Ramsar's registered wetlands are located, is valuable conservation area due to various wetland types and biodiversity. However, due to climate change and other factors, the total area of wetlands has been reduced and biodiversity have been damaged. To mitigate these problems and to manage wetlands efficiently, it is important to identify the factors that change wetlands and to identify how each factor affects them. In this study, we conducted a wetland-related literature analysis in East Asia to derive factors that affect the changes in wetlands, and analyzed the relationships among the factors. Finally we presented research directions considering wetland change factors. In most of the East Asia countries, it was found that there is deficiency in research studies about extraction in direct factors and water-energy infrastructure, tourism & recreation in indirect factors. Also, we presented the necessity for future research using the result between connectivity & relationship analysis and indirect drivers of change and their influence on direct drivers of change. The results of this study could contribute to the establishment of an R&D cooperation system in East Asia region and strengthen wetland management.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.289-309
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• 2022

In recent years, astronomy has been snowballing: including Higgs particle discovery, black hole imaging, extraterrestrial exploration, and deep space observation. Students are also largely interested in astronomy. The purpose of this study is to discover what needs to be improved in the current astronomy curriculum in light of recent scientists' researches and discoveries. We collected keywords from all papers published from 2011 to 2020 in four selected journals-ApJ, ApJL, A&A, and MNRAS- by R package to examine research trends. The curriculum contents were extracted by synthesizing the in-service teachers' coding results in the 2015 revised curriculum document of six subjects (Science, Integrated Science, Earth Science I, Earth Science II, Physics II, Convergence Science). The research results are as follows: first, keywords that appear steadily in astronomy are 'galaxies: formation, galaxy: active, star: formation, accretion, method: numerical.' Second, astronomy curriculum includes all areas except the 'High Energy Astrophysical Phenomena' area within the common science curriculum learned by all students. Third, it is necessary to review the placement of content elements by subject and grade and to consider introducing new concepts based on astronomy research keywords. This is an exploratory study to compare curriculum and the field of scientific research that forms the basis of the subject. We expect to provide implications for a future revision of the astronomy curriculum as a primary ground investigation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.1
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• pp.49-57
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• 2022

In order to efficiently analysis the stability of a slope, measuring the shear strength of soil is needed. The Standard Penetration Test (SPT) is not appropriate for a slope inspection due to cost and weights. One of the ways to effectively measure the N-value is the Dynamic Cone Penetration Test (DCPT). This study was performed to develop a minimized multi-function sensors that can easily estimate CPT values and Volumetric Water Content. N value with multi-fuction sensor DCPT showed -2.5 ~ +3.9% error compared with the SPT N value (reference value) in the field tests. Also, the developed multi-fuction sensor system was tested the correlation between the CPT test and the portable tester with indoor test. The test result showed 0.85 R2 value in soil, 0.83 in weathered soil, and 0.98 in mixed soil. As a result of the field test, the multi-function sensor shows the excellent field applicability of the proposed sensor system. After further research, it is expected that the portable multi-function sensor will be useful for general slope inspection.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.171-182
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• 2022

InSAR (Interferometry SAR) technique is a technique that uses complex data to obtain phase difference information from two or more SAR image data, and enables high-resolution image extraction, surface change detection, elevation measurement, and glacial change observation. In many countries, research on the InSAR technique is being conducted in various fields of study such as volcanic activity detection, glacier observation in Antarctica, and ground subsidence analysis. In this study, a case of large ground settlement due to groundwater level drawdown during tunnelling was introduced, and ground settlement analyses using InSAR technique and numerical analysis method were compared. The maximum settlement and influence radius estimated by the InSAR technique and numerical method were found to be quite similar, which confirms the reliability of the InSAR technique. Through this case study, it was found that the InSAR technique reliable to use for estimating ground settlement and can be used as a key technology to identify the long-term ground settlement history in the absence of measurement data.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.338-355
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• 2022

This study explored the effects of pre-service elementary teachers' inquiries into science class dilemmas. By closely examining the characteristics of the pre-service teachers' inquiry processes and changes in their educational decisions, the effectiveness of using dilemmas as part of teacher education was determined. Twenty fourth-year university pre-service teachers participated and conducted inquiries into science class dilemmas over seven weeks. Based on pre- and post-questionnaires, KWHL tables, inquiry reports, discussions, and group class presentations, the major factors that influence the pre-service teacher's decision-making changes were extracted. The pre-service teachers found the science inquiry process meaningful when exploring the science topics covered in the dilemmas, and claimed that elementary school students would be able to engage in meaningful science explorations if they learned science through inquiry. Furthermore, the pre-service teachers explored the thinking processes and background knowledge of the students in different ways. Documents such as teacher's guides and the curriculum were examined and the students' thought processes were identified through interviews with the teachers and students, which were found to reflect their educational decision-making. Moreover, it was recognized by the pre-service teachers that depending on the situation, alternative teaching methods were possible. The focus on the unstructured dilemma problems provided the pre-service teachers with problem-solving situations that triggered scientific inquiry and exploration of student thinking and revealed the complexity of science teaching and learning. Based on these results, the teacher education implications for using dilemma cases are discussed.