• Land and Housing Review
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• v.13 no.4
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• pp.55-71
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• 2022

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1097-1109
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• 2023

Urban is an area where small-scale changes to individual buildings occur frequently. An existing urban building database requires periodic updating to increase its usability. However, there are limitations in data collection for building changes over a wide urban. In this study, we check the possibility of detecting building changes and updating a building database by using satellite images that can capture a wide urban region by a single image. For this purpose, building areas in a satellite image are first extracted by projecting 3D coordinates of building corners available in a building database onto the image. Building areas are then divided into roof and facade areas. By comparing textures of the roof areas projected, building changes such as height change or building removal can be detected. New height values are estimated by adjusting building heights until projected roofs align to actual roofs observed in the image. If the projected image appeared in the image while no building is observed, it corresponds to a demolished building. By checking buildings in the original image whose roofs and facades areas are not projected, new buildings are identified. Based on these results, the building database is updated by the three categories of height update, building deletion, or new building creation. This method was tested with a KOMPSAT-3A image over Incheon Metropolitan City and Incheon building database available in public. Building change detection and building database update was carried out. Updated building corners were then projected to another KOMPSAT-3 image. It was confirmed that building areas projected by updated building information agreed with actual buildings in the image very well. Through this study, the possibility of semi-automatic building change detection and building database update based on single satellite image was confirmed. In the future, follow-up research is needed on technology to enhance computational automation of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.

• Journal of Radiation Protection and Research
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• v.26 no.2
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• pp.59-66
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• 2001

It is a hot issue to determine the spatial location and shape of tumor boundary in fractionated stereotactic radiotherapy (FSRT). We could get consecutive transaxial plane images from the phantom (paraffin) and 4 patients with brain tumor using helical computed tomography(HCT). K-means classification algorithm was adjusted to change raw data pixel value in CT images into classified average pixel value. The classified images consists of 5 regions that ate tumor region (TR), normal region (NR), combination region (CR), uncommitted region (UR) and artifact region (AR). The major concern was how to separate the normal region from tumor region in the combination area. Relative average deviation analysis was adjusted to alter average pixel values of 5 regions into 2 regions of normal and tumor region to define maximum point among average deviation pixel values. And then we drawn gross tumor volume (GTV) boundary by connecting maximum points in images using semi-automatic contour method by IDL(Interactive Data Language) program. The error limit of the ROI boundary in homogeneous phantom is estimated within ${\pm}1%$. In case of 4 patients, we could confirm that the tumor lesions described by physician and the lesions described automatically by the K-mean classification algorithm and relative average deviation analyses were similar. These methods can make uncertain boundary between normal and tumor region into clear boundary. Therefore it will be useful in the CT images-based treatment planning especially to use above procedure apply prescribed method when CT images intermittently fail to visualize tumor volume comparing to MRI images.

• The Korean Journal of Ecology
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• v.27 no.3
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• pp.165-171
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• 2004

Weight loss and nutrients dynamics during decomposition of oak roots (diameter classes: R₁〈0.2㎝, 0.5㎝〈R₂〈1㎝, 1㎝〈R₃〈2㎝, 2㎝.〈R₄〈4㎝) (Quercus acutissima) were studied for 33-months in Kongiu, Korea. After 33-months, decomposition rate of R₁, R₂, R₃ and R₄ was 49.6%, 47.5%, 66.4% and 66.1%, respectively. The decomposition constant(k) for R₁, R₂, R₃, and R₄ was 0.249/yr, 0.234/yr, 0.397/yr and 0.393/yr, respectively. Larger diameter class of the root lost more weight than smaller diameter class. N concentration in decomposing oak roots increased in all diameter classes. After 33-months, remaining N in R₁, R₂, R₃ and R₄ was 66.5%, 80.7%, 84.4% and 44.4%, respectively. K concentration in decomposing oak roots decreased in early part of decomposition and then increased in later stage of decomposition. After 33-months, remaining P in R₁, R₂, R₃ and R₄ was 64.7%, 62.4%, 93.1% and 30.7%, respectively. K concentration in decomposing oak roots decreased rapidly in early stage of decomposition. Remaining K in R₁, R₂, R₃ and R₄ was 11.6%, 10.6%, 5.9% and 7.7%, respectively. Ca concentration in decomposing oak roots showed different among diameter classes. After 33-months, remaining Ca in R₁, R₂, R₃ and R₄ was 66.2%, 51.0%, 39.1% and 48.3%, respectively. Initial concentration of Mg in oak root was higher in smaller diameter class. After 33-months, remaining Mg in R₁, R₂, R₃ and R₄ was 15.3%, 29.9%, 24.5% and 69.4%, respectively.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of Preventive Medicine and Public Health
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• v.33 no.2
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• pp.208-214
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• 2000

Objectives : Face-to-face interviews were conducted to investigate the relationship between blood pressure and the impairment of cognitive function in rural elderly (N=932) aged 60-64 in Dalsung County, April to September in 1996 Methods : Impairment of cognitive function was defined as a score of less than 23 by the Korean version of the Mini-Mental State Examination (MMSEK). Blood pressure was measured once in each subject using a portable automatic sphygmomanometer. Results : By univariate logistic regression on males, no category of systolic blood pressure bore statistical significance. Groups with diastolic blood pressures of, less than 80 mmHg, 90-94 mmHg, and more than 95mmHg had odds ratios of more than one compared with the reference group (80-89 mmHg). This was most significant in the group with blood pressures lower than 80 mmHg, which had a statistically significant odds ratio of 1.68 (95% confidence interval CI; 1.02-2.75). No category of blood pressure was statistically significant in females. Multivariate logistic regression for males, with adjustment for age, educational attainment, smoking, alcoholic drinking, body mass index, atherosclerotic disease, and antihypertensive medication use, did not alter the odds ratios significantly in terms of systolic blood pressure. However, the group with diastolic blood pressure of less than 80 mmHg had an increased odds ratio of 2.01 (95% CI; 1.15-3.52) compared with the reference group. In females, systolic blood pressure did not alter the odds ratio, but the group with a diastolic blood pressure of less than 80 mmHg had a statistically significant odds ratio of 0.57 (95% CI; 0.37-0.89). Conclusions : These results suggest that the relationship between blood pressure and cognitive function status is stronger diastolic than systolic blood pressure and that there is a complex relationship between blood pressure and cognitive function by sex.

• Sleep Medicine and Psychophysiology
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• v.6 no.1
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• pp.68-75
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• 1999

Objectives: We intended to observe changes in sleep patterns and mood states of night-shift workers following light exposure. We also estimated the degree of tolerance of light exposure. By studying these, we investigated the possibility of applying light therapy to night-shift workers for improving their adaptation. Methods: Twelve night-shift nurses working at Yong-In Mental Hospital volunteered to participate in this study. The study consisted of 3 parts: 1) night-shift control study; 2) light exposure study; 3) day-shift control study. All the nurses accomplished 3 parts of the study, each of which continued for 3 days, except one nurse who did not participate in day-shift control study. During light exposure study, nurses were exposed to bright light for 4 hours from 1AM to 5AM. Sleep patterns were evaluated with wrist actigraphy and automatic sleep analysis program. Mood states and side effects of light exposure were assessed with self-report scales. Results: Sleep period time, total sleep time, and sleep efficiency were increased following light exposure compared with night-shift control study. Light exposure study showed no difference from day-shift control study in above-mentioned sleep parameters. Daily fluctuation of sleep efficiency was less prominent during light exposure study than during night-shift control study. During light exposure study, the subjects felt more elated and energetic in the evening after daytime sleep than during night-shift control study. None of the subjects complained of severe side effects related to light exposure on the third day of light exposure. Tolerance of side effects was noted to develop with the repetition of light exposure. Conclusion: Light exposure improved the daytime sleep of night-shift workers to the level of normal nighttime sleep, making the subjects more elated and energetic. Side effects of light exposure were found to be tolerable. Light exposure seems to be safely applicable to night-shift workers for their adaptation.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.7
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• pp.973-979
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• 2005

To investigate the effect of Pinus densiflora on biochemical parameters in type I diabetic rats, we evaluated the changes of body weight, fasting blood glucose level, oral glucose tolerance test (OGTT) and biochemical parameters after the intraperitoneal injection of distilled solution of Pinus densiflora in streptozotocin (STZ)­induced rats. Thirty-seven male Sprague Dawley rats $(180\pm10g)$ were divided into four groups; diabetic mellitus (DM) group received STZ (50 mg/kg BW, i.v.); low level of pine extract (LP) group received Pinus densiflora (5 mg/kg BW, i.p.), high level of pine extract (HP) group received Pinus densiflora (10 mg/kg BW, i.p.) after the single injection of STZ (50 mg/kg BW, i.v.), respectively. Normal control (NC) group received saline. The change of fasting blood glucose level and OGTT were measured using glucocard II, and the change of biochemical parameter were measured by Automatic Chemistry Analyzer (Hitach-747, Japan). Mean body weight change of DM group was retarded greatly by STZ-exposure. While, body weights of LP and HP groups were progressively increased with some fluctuation, although the increase rates were slower than that of NC group. Fasting blood glucose levels of LP and HP groups were reduced by Pinus densiflora injection, although the fasting blood glucose levels were higher than that of NC group. The results of OGTT was significantly improved in both of LP and HP group compared to DM group. Increases of blood glucose, alanine aminotransferase (ALT), alkaline phosphatase (ALP) and blood urea nitrogen (BUN) levels by STZ-exposure were attenuated by the Pinus densiflora treatment (p<0.05). From the results, it was suggested that Pinus densiflora has a tendency to decrease STZ-induced toxicity in terms of monitoring fasting blood glucose, OGTT and some biochemical parameters of rat.

• Korean Journal of Community Nutrition
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• v.9 no.2
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• pp.191-203
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• 2004

This study was performed to compare the foodservice management practices in the employee feeding operations of Jeonnam and Chungchong area. Questionnaires were developed and mailed to 160 dietitians with management in employee foodservice of Jeonnam and Chungchong area. Completed questionnaires were received from 124 dietitians with a response rate of 77.5%. The results of this study can be summarized as follows:. Age, work experience, concurrent position and work time of dietitians were significantly higher in the Chungchong area than those in the Jeonnam area. Times of meals and amount of meals served per day in the Chungchong area were significantly higher than those in the Jeonnam area. The type of menu by foodservice operation was high non-selective menu in both areas, especially non-selective menu was high in self-operated place whereas selective menu was high in contract management. Period of cycle menu was 10-15 days in Jeonnam area, but that was 7 days in Chungchong area and so cycle menu of both areas was significantly different (p ＜ 0.001). Most considerable factor in menu planning was preference in Jeonnam area and was cost in the Chungchong area. Food purchasing method was used mostly by automatic computerized order in the Jeonnam area whereas telephone or mail order was high in the Chungchong area.