• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.183-196
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• 1996

The purpose of this paper is to explore the possibility of automatic extraction of line feature from Satellite image. The first part reviews the relationship between spatial filtering and cartographic interpretation. The second part describes the principal operations of high frequency filters and their properties, the third part presents the result of filtering application to the SPOT Panchromatic image of the Chinju city. Some experimental results are given here indicating the high feasibility of the filtering technique. The results of the paper is summarized as follows: Firstly the good all-purposes filter dose not exist. Certain laplacian filter and Frei-chen filter were very sensitive to the noise and could not detect line features in our case. Secondly, summary filters and some other filters do an excellent job of identifying edges around urban objects. With the filtered image added to the original image, the interpretation is more easy. Thirdly, Compass gradient masks may be used to perform two-dimensional, discrete differentiation directional edge enhancement, however, in our case, the line featuring was not satisfactory. In general, the wide masks detect the broad edges and narrow masks are used to detect the sharper discontinuities. But, in our case, the difference between the $3{\times}3$ and $7{\times}7$ kernel filters are not remarkable. It may be due to the good spatial resolution of Spot scene. The filtering effect depends on local circumstance. Band or kernel size selection must be also considered. For the skillful geographical interpretation, we need to take account the more subtle qualitative information.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1996.04a
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• pp.254-254
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• 1996

Eosinophils are known to be important effector cells in pathogenesis of asthma. The elucidation of mechanism by which eosinophil survival is regulated in vivo at sites of inflammation is critical tn our understanding of asthma pathogenesis. The maintenance of these cells at site of inflammation depends upon tile balance between its tendency to undergo apoptosis and tile local eosinophil-viability enhancing activity, Qualitative and quantative phenotypic differences have been observed between bronchoalveolar lavage (BAL) and peripheral blood (PB) eosinophils (EOS). We hypothesize that BAL EOS Possess altered functional feature compared to PB EOS. BAL and PB EOS were obtained from ragweed allergic asthmatics after segmental antigen challenge (SAC) at 24 hour or one week, and purified over percoll and CDl6 negative selection. Cells were cultured in duplicate in RPMI, 15% FCS and 1% penicillin/streptomycin without exogenous cytokines. Eosinophil purity and viability was >92%. BAL. EOS viability was 69${\pm}$4.4% versus 39${\pm}$1.6% for PB EOS (p<0.005) at 48 hour time point, and this difference was maintained through day 5 (32${\pm}$7.6% vs. 3.0${\pm}$ 1.4%, p<0.05), Among BAL EOS, those harvested one week after SAC appeared to have an prolonged survival compared to those harvested at 24 hour. Coculture of BAL and PB EOS resulted in significant viability enhancement than expecteed. Direct neutralization of GM-CSF activity, not IL-3 and EL-5, markedly decreased tile survival of BAL EOS in culture, and abrogated tile viability enhancing activity of their culture supernatants in a dose dependent manner. We conclude that BAL EOS activated in vivo possess enhanced viability compared to PB EOS. Mixing and neutralization experiments suggest a role for autocrine production of GM-CSF.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.761-769
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• 2012

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.5
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• pp.31-38
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• 2002

This study attempts to grasp the correlation between the image of bridges and bridge landscapes with their surroundings during day and nighttime viewing, and to understand the psychological influence of nighttime lighting through quantitative analysis. In addition, it presents a design to construct bridges in order to increase viewers enjoyment of bridge landscapes lit at night. To attain this objective and contrive generalization of the results, this paper selects 8 of 9 bridges with lightings in Seoul and excludes bridges constructed by 2004. The criteria for selection of the viewpoints is that each must be within easy reach of bridges, and must allow viewers to recognize surrounding landscape details both in daylight and at night. As well, the pictures of bridges are taken in the terraced land by the riverside. The study selects 16 pictures, judged to be of similar quality and angle, to establish the conditions of luminosity, color, definition and angle. The results are as follows. First, viewers preferences of night landscapes are higher than day landscapes due to the effect of lighting. By day, viewers preferred bridges with various structures such as cable-stayed bridges and arch bridges more than simple bridges like girder bridges. Viewers also indicated preferences for lightings which feature a unique color and which are harmonized with their surroundings. Second, components representing the images of bridge landscape are classified into three types, 'beauty', 'system' and 'agreeableness'. Third, the factors affecting preference are the shape of bridge by day and lighting at night. Esthetic appeal is the most important factor in visual preference so each bridges own esthetic appeal and surroundings must be considered. Thus, a complete plan must be created which considers safety, beauty and the local surroundings. In addition, when the lighting of a bridge is selected, the design of the bridge landscape must consider various lighting schemes to harmonize the upper and lower parts of the structure. At this point, the study reveals the basic elements of bridge planning in order to increase appreciation of the bridge landscape.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.189-202
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• 2004

In this paper, we propose a new salient region detection method in an image. The algorithm is based on the characteristics of human's bottom-up visual attention. Several features known to influence human visual attention like color, intensity and etc. are extracted from the each regions of an image. These features are then converted to importance values for each region using its local competition function and are combined to produce a saliency map, which represents the saliency at every location in the image by a scalar quantity, and guides the selection of attended locations, based on the spatial distribution of saliency region of the image in relation to its Perceptual importance. Results shown indicate that the calculated Saliency Maps correlate well with human perception of visually important regions.

• Journal of Broadcast Engineering
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• v.14 no.3
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• pp.322-331
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• 2009

This paper proposes an efficient data representation of stereo images using edge-based mesh optimization. Mash-based two dimensional warping for stereo images mainly depends on the performance of a node selection and a disparity estimation of selected nodes. Therefore, the proposed method first of all constructs the feature map which consists of both strong edges and boundary lines of objects for node selection and then generates a grid-based mesh structure using initial nodes. The displacement of each nodal position is iteratively estimated by minimizing the predicted errors between target image and predicted image after two dimensional warping for local area. Generally, iterative two dimensional warping for optimized nodal position required a high time complexity. To overcome this problem, we assume that input stereo images are only horizontal disparity and that optimal nodal position is located on the edge include object boundary lines. Therefore, proposed iterative warping method performs searching process to find optimal nodal position only on edge lines along the horizontal lines. In the experiments, we compare our proposed method with the other mesh-based methods with respect to the quality by using Peak Signal to Noise Ratio (PSNR) according to the number of nodes. Furthermore, computational complexity for an optimal mesh generation is also estimated. Therefore, we have the results that our proposed method provides an efficient stereo image representation not only fast optimal mesh generation but also decreasing of quality deterioration in spite of a small number of nodes through our experiments.

• The Journal of Korean society of community based occupational therapy
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• v.3 no.1
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• pp.11-20
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• 2013

Objective : The purpose of this study is to identify the impact of the social support and social network on the quality of life of the elderly residing in a local community. Method : The subjects of this study were 75 healthy old men and women of 13 sites of welfare centers for the disabled and public health centers and senior welfare centers in Busan and Gyeongju. A survey was conducted with a questionnaire that include general characteristics, cognitive ability, social support, social network and quality of life. The analysis was made on 63 replies except 12 subjects who had been excluded by the subject selection criteria. Result : As a result of analyzing correlation of variables affecting life quality, there was positive correlation in contact frequency(p<.05), intimacy(p<.001), and social support(p<.001). Finally, it was analyzed that the variable of intimacy (p<.001) affected life quality of general aged people living in regional community. Conclusion : It was found that intimacy of general aged people living in regional community was a major variable to affect life quality. It could be identified that intimacy which is qualitative feature of social, relational network for the aged who live passive life was important.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.