• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.23-40
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• 2022

Many constructed wetlands have been installed in dam reservoirs nationwide for ecological purification of watershed pollutants, but aging and reduced efficiency are becoming issues. To improve the management of constructed wetlands, an objective evaluation of structural suitability is required. This study evaluated 39 constructed wetlands of 15 dams. First, through fogus group interview(FGI), survey analysis, and analytic hierarchy process(AHP), eight evaluation items in the physical and vegetative aspects were selected and the evaluation criteria applied with weights were prepared. Second, as a result of the structural suitability evaluation, the average score of the overall constructed wetlands was 80.8, with 10 sites rated as 'good grade(91~100)', 22 sites rated as 'normal grade(71~90)' and 7 sites rated as 'poor grade(70 or less)'. The average score of physical structure evaluation was 52.6, with 14 sites rated as 'good', 21 sites as 'normal' and 4 sites as 'poor'. The suitability of location was good level in most constructed wetlands, but the water supply system, depth of water, ratio of length-to-width, and slope of flow channel were evaluated as 'normal' or less in constructed wetlands of 50% or more. Therefore, it was found that overall improvement was necessary for stable flow supply and flow improvement in the constructed wetland. The average score of vegetative structure evaluation was 28.2, and about 84% of them were identified as 'normal' or lower. As a result of analyzing the Spearman's correlation coefficient between the physical structure evaluation score and the vegetation structure evaluation score, there was a significant correlation(r = 0.728, p < 0.001), and it was found that each evaluation factor also influences each other. As a result of the case review of 6 constructed wetlands, the appropriateness of the evaluation results was confirmed, and it was found that the location, flow rate supply, and type of wetland had a great influence on the efficiency and operation of the wetland. Through this study, it will be possible to derive structural weaknesses of constructed wetlands in dam reservoirs as a nature-based solution, to prepare types and practical alternatives for improved management of each constructed wetland in the future, and to contribute to enhancing various environmental functions.

• Journal of the Korea Institute of Building Construction
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• v.22 no.5
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• pp.473-483
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• 2022

BIM(Building Information Modeling) is a technology that can manage information throughout the entire life cycle of the construction industry and serves as a platform for improving productivity and integrating the entire construction industry. Currently, BIM is actively applied in developed countries, and its use at various overseas construction sites is increasing This is unclear. due to air shortening and budget savings. However, there is still a lack of institutional basis and technical limitations in the domestic construction sector, which have led to the lack of utilization of BIM. Various activation measures and institutional frameworks will need to be established for the early establishment of these productive BIMs in Korea. Therefore, as part of the research for the domestic settlement and revitalization of BIM, this study derived a number of key factors necessary for the development of the construction industry through brainstorming and expert surveys using AHP techniques and analyzed the relative importance of each factor. In addition, prior surveys by a group of experts resulted in 1, 3 items in level, 2, 9 items in level, and 3, 27 items in level, and priorities analysis was performed through pairwise comparisons. As a result of the AHP analysis, it was found that the relative importance weight of policy aspects was highest in level 1, and the policy factors in level 2 and the cost-based and incentive system introduction factors were considered most important in level 3. These findings show that the importance of the policy guidance or institutions underlying the activation of BIM rather than research and development or corporate innovation is relatively high, and that the preparation of policy plans by public institutions should be the first priority. Therefore, it is considered that the development of a policy system or guideline must be prioritized before it can be advanced to the next activation stage. The use of BIM technologies will not only contribute to improving the productivity of the construction industry, but also to the overall development of the industry and the growth of the construction industry. It is expected that the results of this study can provide as useful information when establishing policies for activating BIM in central government, relevant local governments, and related public institutions.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.779-793
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• 2023

Intensive and long-term aquaculture activities in Korea have generated considerable amounts of organic matter, deteriorating the sedimentary environment and ecosystem. The Korean government enacted the Fishery Management Act to preserve and manage the environment of fish farms. Based on this, a fisheries environment assessment has been conducted on fish cage farms since 2014, necessitating the development of a scientific and objective evaluation method suitable for the domestic environment. Therefore, a benthic health index (BHI) was developed using the relationship between benthic polychaete communities and organic matter, a major source of pollution in fish farms. In this study, the development process and calculation method of the BHI have been introduced. The BHI was calculated by classifying 225 species of polychaetes appearing in domestic coastal and aquaculture areas into four groups by linking the concentration gradient of the total organic carbon in the sediment and the distributional characteristics of each species and assigning differential weights to each group. Using BHI, the benthic fauna communities were assigned to one of the four ecological classes (Grade 1: Normal, Grade 2: Slightly polluted, Grade 3: Moderately polluted, and Grade 4: Heavily polluted). The application of the developed index in the field enabled effective evaluation of the Korean environment, being relatively more accurate and less affected by the season compared with the existing evaluation methods like the diversity index or AZTI's Marine Biotic Index developed overseas. In addition, using BHI will be useful in the environmental management of fish farms, as the environment can be graded in quantified figures.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Nutrition and Health
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• v.48 no.1
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• pp.46-57
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• 2015

Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M), ${\geq}30%$ (sufficient, S). All data were analyzed on the metabolic risk and nutritional state after application of weighted value and adjustment of sex, age, residential area, income, education, job or jobless, and energy intake using a general linear model or logistic regression. Results: The subjects of group S were 16.9% of total subjects, group M 39.2%, group L 37.6%, and group VL 6.3%. The VL group included more male subjects, younger-aged (19 to 40 years), urban residents, higher income, higher education, and fewer breakfasts eaters together with family members. Among the 4 groups, the VL group showed the highest waist circumference, while the S group showed the lowest waist circumference, body mass index, and serum total cholesterol. The groups of VL and L with lower intake of breakfast energy showed high percent of energy from protein and fat, and low percent of energy from carbohydrate. With the increase of breakfast energy level, intake of energy, most nutrients and food groups increased, and the percentage of subjects consuming nutrients below EAR decreased. The VL group showed relatively higher intake of snacks, sugar, meat and eggs, oil, and seasonings, and the lowest intake of vegetable. Risk of obesity by waist circumference was highest in the VL group by 1.90 times of the S group and the same trend was shown in obesity by BMI. Risk of dyslipidemia by serum total cholesterol was 1.84 times higher in the VL group compared to the S group. Risk of diabetes by Glu-FBS (fasting blood sugar) was 1.57 times higher in the VL group compared to the S group. Conclusion: The results indicate that higher breakfast energy level is positively related to lower metabolic risk and more desirable nutritional state in Korean adults. Therefore, breakfast energy intake more than 30% of their own EER would be highly recommended for Korean adults.

• Journal of Nutrition and Health
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• v.49 no.3
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• pp.165-178
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• 2016

Purpose: This study was performed to examine associations of intakes of milk and dairy products, pulses, and soy foods with bone health in Koreans aged 50 yr and older. Methods: A total of 3,201 men and 3,581 women aged 50 yr and older who participated in the 2008~2011 Korea National Health and Nutrition Examination Survey were grouped by sex and age groups of 50~64 yr and 65 yr and older. Subjects within each sex and age group were divided into three bone health groups: normal, osteopenia, and osteoporosis groups based on bone mineral density. Intakes of nutrients and foods derived from 24-hour recall data were compared among three bone health groups. Associations between intake frequencies of foods, including milk, yogurt, tofu, or soy milk, and osteoporosis risk were evaluated based on confounding risk factor-adjusted logistic regression. Results: Calcium intake was in the order of normal, osteopenia, and osteoporosis in men (p < 0.01) and women (p < 0.05) aged 50~64 yr as well as in men aged 65 yr and older (p < 0.001). In women aged 50~64 yr, intake of milk and dairy products was lower in the osteoporosis group (p < 0.01) as compared with the osteopenia group. Intake of pulses or tofu was not significantly different among bone health groups. Odds ratio (OR) for milk intake frequency (${\geq}2$ times/week) compared to intake frequency less than 1 time/month was 0.45 (95% CI 0.24~0.85, p for trend = 0.022) in men aged 65 yr and older. The OR for yogurt intake frequency (1 time/month~1 time/week) was 0.47 (95% CI 0.30~0.73, p for trend = 0.019) in women aged 50~64 yr. Intake frequency of tofu or soy milk was not associated with reduced risk of osteoporosis in all groups. Conclusion: Dairy food intake was significantly associated with bone health, and its effect was sex- and age group-specific, whereas soy food intake was not. Dietary intervention to prevent osteoporosis would be effective for women aged 50~64 yr old and for men aged 65 yr and older.

• Journal of Nutrition and Health
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• v.49 no.5
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• pp.347-357
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• 2016

Purpose: This study investigated bone mineral density and nutritional state according to consumption of milk in Korean postmenopausal women who drink coffee. Methods: Using the 2008~2009 Korean National Health & Nutrition Examination Survey data, a total of 1,373 postmenopausal females aged 50 yrs and over were analyzed after excluding those with diseases related to bone health. According to coffee and/or milk consumption, subjects were divided into four groups: coffee only, both coffee & milk, milk only, and none of the above. All data were processed after application of weighted values and adjustment of age, body mass index, physical activity, drinking, and smoking using a general linear model. For analysis of nutrient intake and bone density, data were additionally adjusted by total energy and calcium intake. Results: The coffee & milk group had more subjects younger than 65 yrs and higher education, urban residents, and higher income than any other group. The coffee only group showed somewhat similar characteristics as the none of the above group, which showed the highest percentage of subjects older than 65 and in a lower education and socio-economic state. Body weight, height, body mass index, and lean mass were the highest in coffee & milk group and lowest in the none of the above group. On the other hand, the milk only group showed the lowest values for body mass index and waist circumference, whereas percent body fat did not show any difference among the groups. The coffee and milk group showed the highest bone mineral density in the total femur and lumbar spine as well as the highest nutritional state and most food group intakes, followed by the milk only group, coffee only group, and none of the above group. In the assessment of osteoporosis based on T-score of bone mineral density, although not significant, the coffee and milk group and milk only group, which showed a better nutritional state, included more subjects with a normal bone density, whereas the none of the above group included more subjects with osteoporosis than any other group. Conclusion: Bone mineral density in postmenopausal women might not be affected by coffee drinking if their diets are accompanied by balanced food and nutrient intake including milk.