• Land and Housing Review
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• v.8 no.3
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• pp.171-180
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• 2017

In 1990s, as the extreme weather events according to the global warming climate change are occurred frequently all around the world and the scale of the damage increases, the developed countries are promoting various policies for reducing greenhouse gas emissions setting the goal of greenhouse gas reduction with the level of State and local government. Especially for the greenhouse gas reduction of buildings and the inducement of eco-friendly design, the green certification system is strengthened with the assessment of energy and greenhouse management, and recently, according to the policy change of climate and energy, the certification system expanded from the buildings to the level of city and district is piloted. So this research is to understand the main contents and the assessment system of domestic green building certification system implemented in March 2013 in response to the policy change of climate and energy and to suggest the basic data for the improvement of present domestic greenhouse certification standard through the analysis of actual certification and the main case analysis of international green certification system. Recently in developed countries, in 1990s, for the reduction of building's greenhouse gas emission and the inducement of eco-friendly design, from the building of LEED, BREEAM, DGNB to the level of city and district such as LEED Neighborhood Development, BREEAM Communities, DGNB Stadtquartiere, the system is expanded and piloted. On the contrary, as for the domestic standard of green building certification system, the distribution ratio according to the assessment items in each category consists of the assessment system based on the buildings, and just the simple adjustment of some items and the improvement of weighted value are performed. Actually, as a result of selecting the 30 districts of apartment housing with the most certification performance by use among the 49 buildings certified by Institute of Land & Housing from December 2014 to July 2016 and analyzing the assessment score, the certification level is determined by the sectors of high distribution like indoor environment and energy not by the ineffective sector of external environment with low distribution so this system has a limitation to perform the practical means for the policy change of climate and energy. Thus for the national green building certification standards, the assessment system in the sector of external environment is to complemented and furthermore, reflecting domestic reality, through the introduction of certification system and the assessment system with the level of city and district, this system should be improved to meet the international certification standard.

• Particle and aerosol research
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• v.17 no.4
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• pp.91-106
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• 2021

Daily PM_2.5 was collected during summer period in 2020 in Gwangju to investigate its chemical and light absorption properties. In addition, real-time light absorption coefficients were observed using a dual-spot 7-wavelength aethalometer. During the study period, SO₄^2- was the most important contributor to PM_2.5, accounting for on average 33% (10-64%) of PM_2.5. The chemical form of SO₄^2- was appeared to be combination of 70% (NH₄)₂SO₄ and 30% NH₄HSO₄. Concentration-weighted trajectory (CWT) analysis indicated that SO₄^2- particles were dominated by local pollution, rather than regional transport from China. A combination of aethalometer-based and water-extracted brown carbon (BrC) absorption indicated that light absorption of BrC due to aerosol particles was 1.6 times higher than that due to water-soluble BrC, but the opposite result was found in absorption Ångström exponent (AAE) values. Lower AAE value by aerosol BrC particles was due to the light absorption of aerosol BrC by both water-soluble and insoluble organic aerosols. The BrC light absorption was also influenced by both primary sources (e.g., traffic and biomass burning emissions) and secondary organic aerosol formation. Finally the ATR-FTIR analysis confirmed the presence of NH₄⁺, C-H groups, SO₄^2-, and HSO₄^2-. The presence of HSO₄^2- supports the result of the estimated composition ratio of inorganic sulfate ((NH₄)₂SO₄) and bisulfate (NH₄HSO₄).

• Journal of Preventive Medicine and Public Health
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• v.57 no.3
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• pp.288-297
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• 2024

Objectives: This study explored the prevalence and predictors of alcohol and cannabis co-use among 9263 Filipino adolescents, using data from the 2019 Global School-based Student Health Survey (GSHS). Methods: We conducted a cross-sectional secondary analysis of the GSHS, targeting adolescents aged 13-17 years and excluding cases with incomplete data on alcohol and cannabis use. Our analysis employed the bivariate chi-square test of independence and multivariable logistic regression using Stata version 18 to identify significant predictors of co-use, with a p-value threshold set at 0.05. Results: The weighted prevalence of co-users was 4.2% (95% confidence interval [CI], 3.4 to 5.3). Significant predictors included male sex (adjusted odds ratio [aOR], 4.50; 95% CI, 3.31 to 6.10; p<0.001) and being in a lower academic year, specifically grade 7 (aOR, 4.08; 95% CI, 2.39 to 6.99; p<0.001) and grade 8 (aOR, 2.20; 95% CI, 1.30 to 3.72; p=0.003). Poor sleep quality was also a significant predictor (aOR, 1.77; 95% CI, 1.29 to 2.44; p<0.001), as was a history of attempted suicide (aOR, 5.31; 95% CI, 4.00 to 7.06; p<0.001). Physical inactivity was associated with lower odds of co-use (aOR, 0.45; 95% CI, 0.33 to 0.62; p<0.001). Additionally, non-attendance of physical education classes (aOR, 1.48; 95% CI, 1.06 to 2.05; p=0.021), infrequent unapproved parental checks (aOR, 1.37; 95% CI, 1.04 to 1.80; p=0.024), and lower parental awareness of free-time activities (aOR, 0.63; 95% CI, 0.45 to 0.87; p=0.005) were associated with higher odds of co-use. Factors not significantly linked to co-use included age group, being in grade 9, always feeling lonely, having no close friends, being bullied outside school, and whether a parent or guardian understood the adolescent's worries. Conclusions: The findings highlight the critical need for comprehensive interventions in the Philippines, addressing not only physical inactivity and parental monitoring but also focusing on sex, academic grade, participation in physical education classes, sleep quality, and suicide attempt history, to effectively reduce alcohol and cannabis co-use among adolescents.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.191-204
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• 2010

Recently, the CO2 emission and energy consumption have become critical global issues to decide the future of nations. Especially, the spread of IT products and the increased use of internet and web applications result in the energy consumption and CO2 emission of IT industry though information technologies drive global economic growth. EU, the United States, Japan and other developed countries are using IT related environmental regulations such as WEEE(Waste Electrical and Electronic Equipment), RoHS(Restriction of the use of Certain Hazardous Substance), REACH(Registration, Evaluation, Authorization and Restriction of CHemicals) and EuP(Energy using Product), and have established systematic green business/IT strategies to enhance the competitiveness of IT industry. For example, the Japan government proposed the "Green IT initiative" for being compatible with economic growth and environmental protection. Not only energy saving technologies but energy saving systems have been developed for accomplishing sustainable development. Korea's CO2 emission and energy consumption continuously have grown at comparatively high rates. They are related to its industrial structure depending on high energy-consuming industries such as iron and steel Industry, automotive industry, shipbuilding industry, semiconductor industry, and so on. In particular, export proportion of IT manufacturing is quite high in Korea. For example, the global market share of the semiconductor such as DRAM was about 80% in 2008. Accordingly, Korea needs to establish a systematic strategy to respond to the global environmental regulations and to maintain competitiveness in the IT industry. However, green competitiveness of Korea ranked 11th among 15 major countries and R&D budget for green technology is not large enough to develop energy-saving technologies for infrastructure and value chain of low-carbon society though that grows at high rates. Moreover, there are no concrete action plans in Korea. This research aims to deduce the priorities of the Korean green business/IT strategies to use multi attribute weighted average method. We selected a panel of 19 experts who work at the green business related firms such as HP, IBM, Fujitsu and so on, and selected six assessment indices such as the urgency of the technology development, the technology gap between Korea and the developed countries, the effect of import substitution, the spillover effect of technology, the market growth, and the export potential of the package or stand-alone products by existing literature review. We submitted questionnaires at approximately weekly intervals to them for priorities of the green business/IT strategies. The strategies broadly classify as follows. The first strategy which consists of the green business/IT policy and standardization, process and performance management and IT industry and legislative alignment relates to government's role in the green economy. The second strategy relates to IT to support environment sustainability such as the travel and ways of working management, printer output and recycling, intelligent building, printer rationalization and collaboration and connectivity. The last strategy relates to green IT systems, services and usage such as the data center consolidation and energy management, hardware recycle decommission, server and storage virtualization, device power management, and service supplier management. All the questionnaires were assessed via a five-point Likert scale ranging from "very little" to "very large." Our findings show that the IT to support environment sustainability is prior to the other strategies. In detail, the green business /IT policy and standardization is the most important in the government's role. The strategies of intelligent building and the travel and ways of working management are prior to the others for supporting environment sustainability. Finally, the strategies for the data center consolidation and energy management and server and storage virtualization have the huge influence for green IT systems, services and usage This research results the following implications. The amount of energy consumption and CO2 emissions of IT equipment including electrical business equipment will need to be clearly indicated in order to manage the effect of green business/IT strategy. And it is necessary to develop tools that measure the performance of green business/IT by each step. Additionally, intelligent building could grow up in energy-saving, growth of low carbon and related industries together. It is necessary to expand the affect of virtualization though adjusting and controlling the relationship between the management teams.

• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.393-407
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• 2012

This study suggests the results of temporal and spatial variations for rainfall data in the Korean Peninsula. We got the index of the rainfall amount, frequency and extreme indices from 65 weather stations. The results could be easily understood by drawing the graph, and the Mann-Kendall trend analysis was also used to determine the tendency (up & downward/no trend) of rainfall and temperature where the trend could not be clear. Moreover, by using the FARD, frequency probability rainfalls could be calculated for 100 and 200 years and then compared each other value through the moment method, maximum likelihood method and probability weighted moments. The Average Rainfall Index (ARI) which is meant comprehensive rainfalls risk for the flood could be obtained from calculating an arithmetic mean of the RI for Amount (RIA), RI for Extreme (RIE), and RI for Frequency (RIF) and as well as the characteristics of rainfalls have been mainly classified into Amount, Extremes, and Frequency. As a result, these each Average Rainfall Indices could be increased respectively into 22.3%, 26.2%, and 5.1% for a recent decade. Since this study showed the recent climate change trend in detail, it will be useful data for the research of climate change adaptation.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.110-125
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• 2011

A number of the Bronze Age dwelling sites have been found and excavated in the Yoodoocheon, Onyangcheon and Baekseokdong basins. Two basins are located near Asan and Onyan in the Chungnam Province of South Korea. Baekseokdong is located in Cheonan, Chungnam. 207 dwelling sites are concentrated around the area of $1.3km^2$ in the Baekseokdong. 177 dwelling sites are sparse and distributed over the area of $1.3km^2$ in the Yongdoocheon and Onyangcheon basins. Most of the Bronze Age dwelling sites in those areas located on the hill. The hills have similar geomorphological environments except for slight differences in geological faces. This study analyzes geomorphological environments of the high-density residential zone of the Bronze Age in the Yoodoocheon and the Onyangcheon basins, and then compares them with the results in Baekseokdong. Study results show that high-density residential zone consists mainly of specific micro-landforms such as the Crest slope, the Crest flat and the Upper side slope, and southeast-facing aspect. A lot of Gentle slope lands were distributed in terms of terrain slope but it is far from specific geomorphological environments. This is not weighted in specific value. Our results show that the geomorphological characteristic derived from this study is major considerations to develop dwelling sites in the Bronze Age. This can be useful to discover the possible dwelling sites over other Chungnam hill regions.

• Korean Journal of Ecology and Environment
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• v.40 no.1
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• pp.93-102
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• 2007

The runoff characteristics of organic matter in turbid water were investigated in eleven tributary streams of the Han River system, Korea. The flow-weighted event mean concentrations of organic matter ranged from 1.5 to 3.2 mg $L^{-1}$ of DOM and 2.2 of 29.1 mg $L^{-1}$ of POM, respectively. The SUVA value which reflects the proportion of humic substance in organic matters was higher during the rainfall season, meaning that the runoff of refractory form increase in this period. Stable carbon isotope ratios of both POM and DOM were different among streams, which reflect the sources of organic matter. DOM isotope ratios were less depleted of $^{13}C$ than that of POM by approximately 1 to $2%_{\circ}$ ${\delta}^{13}C$ of the several turbid streams (the Mandae Stream, the Jawoon Stream, and the Daegi stream) were heavier than those of clear streams. ${\delta}^{13}C$ values in the turbid upstream tributaries were similar to those of downstream reaches (such as the Soyang River, the Sum River, and the Seo River). From the ${\delta}^{13}C$ analysis of POM it could be calculated that $C_4$ pathway contributed approximately 15.9 to 23.6% of organic matter in several turbid upstream sites, and over 20% in the three sites of large downstream reaches. On the contrary it contributed only 9.1 to 12.8% in clear streams of forest watersheds. In the Soyang River, $C_4$ pathway organic matter contributed 8.8% of the DOM pool.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.69-84
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• 1997

We consider a linear calibration problem, $y_i = $$\alpha + \beta (x_i - x_0) + \epsilon_i$, $i=1, 2, {\cdot}{\cdot},n$ $y_f = \alpha + \beta (x_f - x_0) + \epsilon, $ where we observe $(x_i, y_i)$'s for the controlled calibration experiments and later we make inference about $x_f$ from a new observation $y_f$. The objective of the calibration design problem is to find the optimal design $x = (x_i, \cdots, x_n$ that gives the best estimates for $x_f$. We compare Kim(1989)'s Bayesian design which minimizes the expected value of the posterior variance of $x_f$ and some optimal designs from literature. Kim suggested the Bayesian optimal design based on the analysis of the characteristics of the expected loss function and numerical must be equal to the prior mean and that the sum of squares be as large as possible. The designs to be compared are (1) Buonaccorsi(1986)'s AV optimal design that minimizes the average asymptotic variance of the classical estimators, (2) D-optimal and A-optimal design for the linear regression model that optimize some functions of $M(x) = \sum x_i x_i'$, and (3) Hunter & Lamboy (1981)'s reference design from their paper. In order to compare the designs which are optimal in some sense, we consider two criteria. First, we compare them by the expected posterior variance criterion and secondly, we perform the Monte Carlo simulation to obtain the HPD intervals and compare the lengths of them. If the prior mean of $x_f$ is at the center of the finite design interval, then the Bayesian, AV optimal, D-optimal and A-optimal designs are indentical and they are equally weighted end-point design. However if the prior mean is not at the center, then they are not expected to be identical.In this case, we demonstrate that the almost Bayesian-optimal design was slightly better than the approximate AV optimal design. We also investigate the effects of the prior variance of the parameters and solution for the case when the number of experiments is odd.

• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.386-396
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• 2020

Objective : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion : This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.