• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제30권2호
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• pp.189-197
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• 2012

This research attempted to find possibilities of the practical use utilizing geospatial methods for the balanced promotion of sustainable coastal development and preservation through a case study of Jekyll Island, one of Georgia's barrier islands. In response, this research provided ways for practical use in sustainable development and preservation plans. First this research thoroughly investigated the 1996 master plan of Jekyll Island and tried to recalculate developed and undeveloped areas. Second, new estimations for developed areas were investigated through field survey. Third, this research proposed the use of the satellite images with different levels of spatial resolutions and tested different classification schemes to find possibilities for practical use. For these purposes, first, we classified developed and undeveloped features by manual digitization using an aerial photo image with 0.5m spatial resolution. Second, a Landsat 7 ETM+ and a QuickBird satellite images with mid- and high-levels of spatial resolutions were applied to identify developed and undeveloped areas using both the National Land Cover Data (NLCD) and the Coastal Change Analysis Program (CCAP) classification schemes. Also, GEOBIA (Geographic Object-Based Image Analysis) was conducted to accurately identify developed and undeveloped areas.

• Journal of Information Technology Services
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• 제18권1호
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Journal of Korea Multimedia Society
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• 제10권7호
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• pp.856-868
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• 2007

We propose an effective method for block classification in a document image. The gray level document image is converted to the binary image for a block segmentation. This binary image would be smoothed to find the locations and sizes of each block. And especially during this smoothing, the inner block heights of each block are obtained. The gray level image is divided to several blocks by these location informations. The SGLDM(spatial gray level dependence matrices) are made using the each gray-level document block and the seven second-order statistical texture features are extracted from the (0,1) direction's SGLDM which include the document attributes. Document image blocks are classified to two groups, text and non-text group, by the inner block height of the block at the nearest neighbor rule. The seven texture features(that were extracted from the SGLDM) are used for the five detail categories of small font, large font, table, graphic and photo blocks. These document blocks are available not only for structure analysis of document recognition but also the various applied area.

• Journal of the Korea Convergence Society
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• 제11권2호
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• pp.17-22
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• 2020

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

• Child Health Nursing Research
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• 제7권2호
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• pp.161-178
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• 2001

The purpose of this study was to identify nursing interventions performed by neonatal nursing unit nurses. For data collection this study used the taxonomy of Nursing Intervention Classification(NIC : 486 nursing intervention) which was modified by McCloskey & Bulecheck(2000). The new 58 nursing interventions was translated into Korean, and then modified by pannel group, which consist of clinical experts and nursing scholars and finally the 419 nursing interventions was selected. The data were collected from 112 nurses. 168 nursing interventions were performed at least monthly by 50％ or more of the nurses. The high frequency of performed nursing interventions were Family domain. 37 nursing interventions were performed at least once a day. The nursing interventions receiving the highest item mean score were neonatal care, neonatal monitoring, photo-therapy; neonate, bottle feeding and temperature regulation. 56 nursing interventions were rarely performed by 90％ or more of the nurses. Most of them were in the behavioral domain. The rarely used interventions were urinary bladder training, art therapy, religious addiction prevention, religious ritual enhancement and bladder irrigation. Therefore, neonatal nursing units nurses used interventions in the Physiological: basic domain most often on a daily basis and the interventions in the behavioral domain least often. These findings will help in building of a standardized language for the neonatal nursing units and enhance the quality of nursing care. Further study will be needed to classify each intervention class and nursing activity and validate NIC in pediatric care unit.

• Journal of Korean Society for Geospatial Information Science
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• 제20권2호
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• pp.73-79
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• 2012

This study attempts to analyze changes in land use patterns in a compound urban and agricultural city Kimje-si, using LANDSAT TM imagery and to forecast future changes accordingly. As a new approach to supervised classification, HSB(Hue, Saturation, Brightness)-transformed images were used to select training zones, and in doing so classification accuracy increased by more than 5 percent. Land use changes were forecasted by using a cellular automaton algorithm developed by applying Markov Chain techniques, and by taking into account classification results and GIS data, such as population of the pertinent region by area, DEMs, road networks, water systems. Upon comparing the results of the forecast of the land use changes, it appears that geographical features had the greatest influence on the changes. Moreover, a forecast of post-2030 land use change patterns demonstrates that 21.67 percent of mountain lands in Kimje-si is likely to be farmland, and 13.11 percent is likely to become city areas. The major changes are likely to occur in small mountain lands located in the heart of the city. Based on the study result, it seems certain that forecasting future land use changes can help plan land use in a compound urban and agricultural city to procure food resources.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제49권2호
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• pp.1-7
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• 2012

Generally, correction methods for faded images use illuminant estimation algorithms, such as the gray world assumption and white patch Retinex methods, as the phenomenon of color fading is regarded as an illuminant effect. However, this induces inaccurate faded color correction, as images fade at different rates according to the ink property, temperature, humidity, and illuminant. Therefore, this paper presents a color correction method for faded images using classification in LCybCrg color space. The input faded image is first separated according to the chromaticity based on LCybCrg opponent color space. The faded color correction is then performed based on the gray world assumption in RGB color space. Thereafter, weights calculated from CybCrg values are applied to reduce contour artifacts. As a result, the proposed method provides better color correction for faded images than previous methods.

• Journal of the Korean Home Economics Association
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• 제42권6호
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• pp.103-110
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• 2004

Among accessaries, headwear is important to protect one's face and head from the sun, wind and cold. This study was performed to provide fundamental data on college female students' head shape by classifying their head shapes and by analyzing the characteristics of each head shape in order to improve the fitness of headwear. The subjects were 193 Korean college female students. The subjects were directly measured anthropometrically and indirectly analyzed photo-graphically. By direct and indirect measurement, 5 factors were extracted through factor analysis and those factors comprised 76.34% of the total variance. 3 clusters as their head shape were categorized using 5 factor scores by cluster analysis. Type 1 was characterized by long head type and having smallest head thickness and head girth. Type 2 had average size and the most round-head type. Type 3 was characterized by short head type and the largest head thickness and head girth.

• Journal of the Korean Society of Clothing and Textiles
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• 제26권10호
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• pp.1412-1423
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• 2002

The purpose of this study is to objectively explain the visual sensibility of clothing torture that satisfies the consumer's sensibility. The photo stimuli on clothing texture are divided into hard, soft transparent and brilliant. For the study of image 38 kinds of costume samples is used. The Study was measured by using Semantic Differential method. The subjects were 410 females in twenties. The data were analyzed by factor analysis, ANOVA, MDS and regression analysis. Data were analyzed by SPSS. The major findings of this research were as follows: 1. As a result of the factor analysis,5 factors of visual sensibility were consist of high qualities, touches, looks, lightness, and warmness or coolness.2. There were significant difference in visual sensibility based on classification of clothing texture.3. The clothing texture was classified as thin-full, flat-lumpy. 4. As a result of the regression analysis, preferences of consumers can be connected directly with buying behavior and satisfaction can be closely related with preferences and positive buying behavior.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2009년도 춘계학술발표대회
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• pp.98-101
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• 2009

디지털 카메라의 사용이 일반화됨에 따라 수많은 디지털 사진들이 새롭게 생성되고 있다. 수많은 사진을 사용자가 직접 분류하고 앨범화하는 것은 많은 노력과 시간이 소요되는 불편한 작업이 되고 있다. 최근 들어서는 카메라의 높은 보급률로 인해 하나의 행사에 있어서도 여러대의 카메라가 사용되는 등 대용량 사진을 효과적으로 분류하고 시각화할 수 있는 방법의 필요성이 증가하고 있다. 개인적인 사진 관리에 있어서 뿐만 아니라 여러명의 촬영한 사진의 분류 및 제 3 자에게 사진이 촬영된 내용을 소개하기 위한 목적 등 다양한 방면에서 사진 클러스터링 및 시각화와 관련된 기법이 사용될 수 있다. 본 논문에서는 이와 관련된 이전 연구들을 살펴보고 개발중인 사진 분류 및 시각화 시스템의 프로토타입을 소개하며 기존 방법들과 차별화되는 사진 처리 기법에 대해서 살펴본다.