• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.3
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• pp.147-154
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• 2011

Decision making in Environmental Impact Assessment (EIA) and Consultation on the Coastal Area Utilization (CCAU) is footing on the survey reports, thus requires concrete and accurate information on the natural habitats. In spite of the importance of reporting the ecological quality and status of habitats, the accumulated knowledge and recent techniques in ecology such as the use of investigated cases and indicators/indices have not been utilized in evaluation processes. Even the EIA report does not contain sufficient information required in a decision making process for conservation and development. In addition, for CCAU, sampling efforts were so limited that only two or a few stations were set in most study cases. This hampers transferring key ecological information to both specialist review and decision making processes. Hence, setting the effective number of sampling stations can be said as a prior step for better assessment. We introduced a few statistical techniques to determine the number of sampling stations in macrobenthos surveys. However, the application of the techniques requires a preliminary study that cannot be performed under the current assessment frame. An analysis of the spatial configuration of sampling stations from 19 previous studies was carried out as an alternative approach, based on the assumption that those configurations reported in scientific journal contribute to successful understanding of the ecological phenomena. The distance between stations and number of sampling stations in a $4{\times}4$ km unit area were calculated, and the medians of each parameter were 2.3 km, and 3, respectively. For each study, approximated survey area (ASA, $km^2$) was obtained by using the number of sampling stations in a unit area (NSSU) and total number of sampling stations (TNSS). To predict either appropriate ASA or NSSU/TNSS, we found and suggested statistically significant functional relationship among ASA, survey purpose and NSSU. This empirical approach will contribute to increasing sampling effort in a field survey and communicating with reasonable data and information in EIA and CCAU.

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.39-53
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• 2007

Generally, fractured medium can be described with some key parameters, such as hydraulic conductivities or random field of hydraulic conductivities (continuum model), spatial and statistical distribution of permeable fractures (discrete fracture network model). Investigating the practical applicability of the well-known conceptual models for the description of groundwater flow in fractured media, various types of hydraulic tests were applied to studies on the highly fractured media in Geumsan, Korea. Results from single-hole packer test show that the horizontal hydraulic conductivities in the permeable media are between $7.67{\times}10^{-10}{\sim}3.16{\times}10^{-6}$ m/sec, with $7.70{\times}10^{-7}$ m/sec arithmetic mean and $2.16{\times}10^{-7}$ m/sec geometric mean. Total number of test interval is 110 at 8 holes. The number of completely impermeable interval is 9, and the low permeable interval - below $1.0{\times}10^{-8}$ m/sec is 14. In other words, most of test intervals are permeable. The vertical distribution of hydraulic conductivities shows apparently the good correlation with the results of flowmeter test. But the results from the cross-hole test show some different features. The results from the cross-hole test are highly related to the connectivity and/or the binary properties of fractured media; permeable and impermeable. From the viewpoint of the connection, the application of the general stochastic approach with a single continuum model may not be appropriate even in the moderately or highly permeable fractured medium. Then, further studies on the investigation method and the analysis procedures should be required for the reasonable and practical design of the conceptual model, with which the binary properties, including permeable/impermeable features, can be described.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.115-127
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• 2018

This study aims to propose management strategies of ventilation paths for improving urban thermal environments. For this purpose, Gimhae-si in Gyeongsangnamdo was selected as a study area. We analyzed hot spots and cool spots in Gimhae by using Landsat 8 satellite image data and spatial statistical analysis, and finally derived the vulnerable areas to thermal environment. In addition, the characteristics of ventilation paths including wind direction and wind speed were analyzed by using data of the wind resource map provided by Korea Meteorological Administration. As a result, it was found that a lot of hot spots were similar to those with weak wind such as Jinyoung-eup, Jillye-myeon, Juchon-myeon and the downtown area. Based on the analysis, management strategies of ventilation paths in Gimhye were presented as follows. Jinyoung-eup and Jillye-myeon with hot spot areas and week wind areas have a strong possibility that hot spot areas will be extended and strengthened, because industrial areas are being built. Hence, climate-friendly urban and architectural plans considering ventilation paths is required in these areas. In Juchon-myeon, where industrial complexes and agricultural complexes are located, climate-friendly plans are also required because high-rise apartment complexes and an urban development zone are planned, which may induce worse thermal environment in the future. It is expected that a planning of securing and enlarging ventilation paths will be established for climate-friendly urban management. and further the results will be utilized in urban renewal and environmental planning as well as urban basic plans. In addition, we expect that the results can be applied as basic data for climate change adaptation plan and the evaluation system for climate-friendly urban development of Gimhye.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.272-279
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• 2007

Purpose: The aim of this study was to evaluate the discriminating nature of $^{123}I-FP-CIT$ SPECT in patients with parkinsonism. Methods: $^{123}I-FP-CIT$ SPECT images acquired from the 18 normal controls; NC ($60.4{\pm}10.0$ yr) and 237 patients with parkinsonism ($65.9{\pm}9.2$ yr) were analyzed. From spatialIy normalized images, regional counts of the caudate, putamen, and occipital lobe were obtained using region of interest method. Binding potential (BP) was calculated with the ratio of specific to nonspecific binding activity at equilibrium. Additionally, the BP ratio of putamen to caudate (PCR) and asymmetric Index (ASI) were measured. Results: BPs of NC $3.37{\pm}0.57,\; 3.10{\pm}0.41,\; 3.23{\pm}0.48$ for caudate, putamen, whole striatum, respectively) had no significant difference with those of essential tremor; ET ($3.31{\pm}0.64,\; 3.06{\pm}0.61,\; 3.14{\pm}0.63$) and Alzheimer's disease; AD (3.33 $\pm$0.60, 3.29$\pm$0.79, 3.31$\pm$0.70), but were higher than those of Parkinson's disease; PD (1.92$\pm$0.74, 1.39$\pm$0.68, 1.64$\pm$0.68), multiple system atrophy; MSA (2.36$\pm$1.07, 2.16$\pm$0.91, 2.26$\pm$0.96), and dementia with Lewy body; DLB (1.95$\pm$0.72, 1.64$\pm$0.65, 1.79$\pm$0.66)(p<0.005). PD had statisticalIy lower values of PER and higher values of ASI than those of NC (p<0.005). And PD had significantIy lower value of PCR, higher ASI and lower BP in the putamen and whole striatum than MSA (p<0.05). Conclusion: Dopamine transporter image of $^{123}I-FP-CIT$ SPECT was a good value in differential diagnosis of parkinsonism.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.418-430
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• 2017

The quality of surface water is a very important issue to use various demands like as drinking water, industrial, agricultural and recreational usages. There has been an increasing demand for monitoring water quality of many rivers by regular measurements of various water quality variables. However precise and effective monitoring is not enough, if the acquired dataset is not analyzed thoroughly. Therefore, the aim of this study was to estimate differences of seasonal and regional water quality using multivariate data analysis for each investing tributaries in Han River. Statistical analysis was applied to the data concerning 11 mainly parameters (flow, water temperature, pH, EC, DO, BOD, COD, SS, TN, TP and TOC) for the time period 2012~2016 from 12 sampling sites. The seasonal water quality variations showed that each of BOD, TN, TP and TOC average concentration in spring and winter was higher than that of summer and fall, respectively. In summer each flow rate and average concentration of SS was higher than any other seasons, respectively. The correlation analysis were explained that EC had a strong relationship with BOD (r=0.857), COD (r=0.854), TN (r=0.899) and TOC (r=0.910). According to principal component analysis, five principal components (Eigenvalue > 1) are controlled 98.0% of variations in water quality. The first component included TP, DO, pH. The second component included EC, TN. The third component included SS. The fourth component included flow. The last component included Temp. Cluster analysis classified that spring is similar to fall and winter with water quality parameters. AnyA, WangsA, JungrA and TancA were identified as affected by organic pollution. Cluster analysis derived seasonal differences with investigating sites and better explained the principal component analysis results.

• Journal of Korean Home Economics Education Association
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• v.20 no.1
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• pp.101-116
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• 2008

Most who were polled stated that they use the internet everyday. Also, it is undeniable that Internet has become one of the popular shopping markets with the spatial-convenience and time-saving it provides. With the growth of Internet and Internet shopping malls, effects on clothing purchasing of adolescents. The purpose of this study was to investigate the clothing shopping orientation and Internet clothing purchasing type of high school students. Subjects were 685(male 354, female 331) high school students located in Jeollabukdo province. In this statistical analysis, SPSS 11.5 for Windows Program. These data were analyzed by factor analysis, $x^2$ test, t-test, One-way Anova, Duncan' multiple range, Pearson's correlation coefficient. The results of this study were as follows. Six dimensions of clothing shopping orientation were derived by factor analysis: fashion hedonic shopping brand ostentation time convenience economic esthetics orientation. The clothing purchasing type in Internet had 3 factors(convenience active impulse buying economic pursuit). The groups were significantly different in regard to clothing shopping orientation, clothing purchasing type in Internet shopping mall according to demographic variables(gender, educational background of parents, a school record). Clothing shopping orientation variables had positive correlations except of hedonic shopping economic orientation with clothing purchasing type in Internet. As a conclusion, high school students' shopping orientation and purchasing type of apparel in Internet shopping mall constituted important characteristics which could affect directly Internet purchase behavior of adolescents. These results should be fundamental information for clothing and textile education in secondary school.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.101-116
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• 2012

Accident prediction models have been utilized to predict accident possibilities in existing or projected freeways and to evaluate programs or policies for improving safety. In this study, a traffic accident prediction model for freeways was developed for the above purposes. When selecting variables for the model, the highest priority was on the ease of both collecting data and applying them into the model. The dependent variable was set as the number of total accidents and the number of accidents including casualties in the unit of IC(or JCT). As a result, two models were developed; the overall accident model and the casualty-related accident model. The error structure adjusted to each model was the negative binomial distribution and the Poisson distribution, respectively. Among the two models, a more appropriate model was selected by statistical estimation. Major nine national freeways were selected and five-year dada of 2003~2007 were utilized. Explanatory variables should take on either a predictable value such as traffic volumes or a fixed value with respect to geometric conditions. As a result of the Maximum Likelihood estimation, significant variables of the overall accident model were found to be the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volume to the number of curved segments between ICs(or JCTs). For the casualty-related accident model, the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volumes had a significant impact on the accident. The likelihood ratio test was conducted to verify the spatial and temporal transferability for estimated parameters of each model. It was found that the overall accident model could be transferred only to the road with four or more than six lanes. On the other hand, the casualty-related accident model was transferrable to every road and every time period. In conclusion, the model developed in this study was able to be extended to various applications to establish future plans and evaluate policies.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.1
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• pp.12-16
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• 2015

Purpose Principal component analysis (PCA) is a method often used in the neuroimagre analysis as a multivariate analysis technique for describing the structure of high dimensional correlation as the structure of lower dimensional space. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly independent variables called principal components. In this study, in order to investigate the usefulness of PCA in the brain PET image analysis, we tried to analyze C[11]-PIB PET image as a representative case. Materials and Methods Nineteen subjects were included in this study (normal = 9, AD/MCI = 10). For C[11]-PIB, PET scan were acquired for 20 min starting 40 min after intravenous injection of 9.6 MBq/kg C[11]-PIB. All emission recordings were acquired with the Biograph 6 Hi-Rez (Siemens-CTI, Knoxville, TN) in three-dimensional acquisition mode. Transmission map for attenuation-correction was acquired using the CT emission scans (130 kVp, 240 mA). Standardized uptake values (SUVs) of C[11]-PIB calculated from PET/CT. In normal subjects, 3T MRI T1-weighted images were obtained to create a C[11]-PIB template. Spatial normalization and smoothing were conducted as a pre-processing for PCA using SPM8 and PCA was conducted using Matlab2012b. Results Through the PCA, we obtained linearly uncorrelated independent principal component images. Principal component images obtained through the PCA can simplify the variation of whole C[11]-PIB images into several principal components including the variation of neocortex and white matter and the variation of deep brain structure such as pons. Conclusion PCA is useful to analyze and extract the main pattern of C[11]-PIB image. PCA, as a method of multivariate analysis, might be useful for pattern recognition of neuroimages such as FDG-PET or fMRI as well as C[11]-PIB image.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.649-663
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• 2019

The 2018 United Nations Development Programme (UNDP) report announced that deforestation in North Korea is the most extreme situation and in terms of climate change, this deforestation is a global scale issue. To respond deforestation, various study and projects are conducted based on remote sensing, but access to public data in North Korea is limited, and objectivity is difficult to be guaranteed. In this study, the forest detection based on density estimation in statistic using Landsat imagery was conducted in Gangwon province which is the only administrative district divided into South and North. The forest spatial data of South Korea was used as data for the labeling of forest and Non-forest in the Normalized Difference Vegetation Index (NDVI), and a threshold (0.6658) for forest detection was set by Gaussian Probability Density Function (PDF) estimation by category. The results show that the forest area decreased until the 2000s in both Korea, but the area increased in 2010s. It is also confirmed that the reduction of forest area on the local scale is the same as the policy direction of urbanization and industrialization at that time. The Kappa value for validation was strong agreement (0.8) and moderate agreement (0.6), respectively. The detection based on the Gaussian PDF estimation is considered a method for complementing the statistical limitations of the existing detection method using satellite imagery. This study can be used as basic data for deforestation in North Korea and Based on the detection results, it is necessary to protect and restore forest resources.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.