• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.6
• /
• pp.113-119
• /
• 2001

In this paper, we propose an improved watershed algorithm that resolves the oversegmentation problem shown in the previous watershed algorithm and its modifications when the spatial video segmentation is performed. The principal idea of the proposed algorithm is merging the shallow catchment basin whose depth is less than a given threshold into the deeper one during flooding step. In the flooding process, the growth of the existing catchment basins and the extraction of newly flooded ones are accomplished. We present the experimental results using several MPEG test sequences in the last part of the paper. As a consequence, the proposed algorithm shows good segmentation results according to the thresholds applied by adding very small amount of calculations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.6
• /
• pp.679-688
• /
• 2009

Worldwide exploring and research for manganese nodules, as new energy resource, distributed on the deep seabed have progressed recently. Korea Ocean Research & Development Institute(KORDI) is a central organization to exploit the manganese nodules in the Pacific Ocean with 5,000m depth. Precise exploration is required for estimating amount of recoverable deposit, and this task could be accomplished by processing digital image processing techniques to the images taken by underwater camera system. Image processing and analysis provide information about characteristics of distribution of the manganese nodules. This study proposed effective methods to remove vignetting effect to improve image quality and to extract information. The results show more reliable information could be obtained by removing the vignetting and feasibility of utilizing image processing techniques for exploring the manganese nodules.

• The Journal of Korean Academy of Prosthodontics
• /
• v.32 no.4
• /
• pp.593-607
• /
• 1994

The purpose of this study is to evaluate the effect of the bone morphogenetic protein, bone matrix gelatin and collagen matrix on the amount and shape of generating new bone adjacent to the implant. Implants were inserted in the mandible of adult dogs at 2 months after teeth extraction. Artificial bony defects, 3mm in width and 4mm in depth were made at the mesial and distal side of implant. Experimental groups were divided into three groups ; Group 1 : Defects filled with collagen matrix and bone morphogenetic protein, Group 2 : Defects filled with bone matrix gelatin. Control group : Defects filled with only collagen matrix. After implantation, the animals were sacrificed at 1,3,5 and 10 weeks for light microscopic examination. For the fluorescent microscopic examination. each tertracycline Hcl and calcein were injected at 1, 3, 5, 8 and 10 weeks after implantation. The results obtained were as follows : 1. The molecular weight of bovine BMP was about 18,100 by hydroxyapatite chromatography. 2. Osseointegration was observed in experimental groups 1 & 2, and BMG and BMP had an excellent bone forming capability as a filling materials to the repair of the bone defects. 3. The degree of healing of bone defect area, the experimental group 1 showed more prominent bone formation than control group, and the control group showed fibrous connective tissue between the implant and the bone. 4. In the fluorescent microscopic findings, bone remodelling was observed regenerative lamellar bone at defect area in experimental group 1, and partial remodelling in experimental group 2, In the control group, fibrous connective tissue was observed between the implant and bone surface and sign of remodelling was not apperaed. Above results suggest that BMP has rapid osteoinductive property and can be used clinically as a bone substitute on bone defects around implants.

• The Journal of Korean Academy of Prosthodontics
• /
• v.30 no.3
• /
• pp.361-378
• /
• 1992

The purpose of this investigation was to evaluate the effect of the porous hydroxyapatite particles (Interpore $200^{(R)}$) and guided tissue regeneration membrane ($Gore-Tex^{TM}$ augmentation material) on amount and shape of generating new bone adjacent to implant. Implants were placed immediately after extraction in the bilateral 3rd, 4th premolars of the mandible of the adult dogs. In all experimental groups, artificial bony defects were formed at the buccal cortex area, 3.3mm in width and 3.0mm in depth. In the control group : sutured without HA particles & membranes after placing implants, the experimental group 1 : membrane was place over the artificial bony defect, the experimental group 2 : bony defect was filled with HA particles and covered with membrane. The examination of bone-implant interfaces using light microscope and fluorescent microscope concluded as follows. 1. In all three experimental groups, osseointegration was observed without epithelial migration. 2. In the healing degree of bony defect area, the experimental group 1, 2 showed more prominent healing than control group, and the experimental group 1 showed the most excellent bone formation. 3. In fluorescent microscopic finding, bone remodeling was observed in regenerated bone tissue at defect area of experimental group 1, but in experimental group 2, irregular, discontinuous linear fluorescence was observed at the lower portion of defect area and sign of bone remodeling was weak.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.5
• /
• pp.279-284
• /
• 2017

Currenty, in the era of big data, text mining and opinion mining have been used in many domains, and one of their most important research issues is to extract significant information from social media. Thus in this paper, we propose a logistic regression ensemble method of finding the main body text from blog HTML. First, we extract structural features and text features from blog HTML tags. Then we construct a classification model with logistic regression and ensemble that can decide whether any given tags involve main body text or not. One of our important findings is that the main body text can be found through 'depth' features extracted from HTML tags. In our experiment using diverse topics of blog data collected from the web, our tag classification model achieved 99% in terms of accuracy, and it recalled 80.5% of documents that have tags involving the main body text.

• Women's Health Nursing
• /
• v.26 no.2
• /
• pp.141-150
• /
• 2020

Purpose: High-quality nursing care must be provided for women with infertility, and their nursing needs must be identified. Although scales have been developed to assess infertility-related stress, quality of life, and psychosocial status, there is a lack of scales that assess the nursing needs of women with infertility. The purpose of this study was to develop a needs assessment scale for nursing care in women with infertility and to verify its reliability and validity. Methods: The 250 subjects in this study were women with infertility recruited from four hospitals. The scale was developed following the framework of DeVellis, through a literature review, in-depth interviews, development of preliminary items, verification of content validity, development of secondary items, verification of construct validity, and extraction of the final items. Date were analyzed using item analysis, factor analysis, confirmatory factor analysis, Pearson correlation coefficients, and Cronbach's alpha. Reliability was tested using Cronbach's alpha, and validity was evaluated using item analysis, exploratory factor analysis, and criterion validity. Results: The final version of the nursing needs assessment scale for woman with infertility consisted of 18 items. Four factors (physical and psychological nursing needs, needs for information regarding treatment, needs for infertility-related understanding and concern, and supportive needs) explained 66.0% of the total variance. Cronbach's alpha was .92 for the overall instrument and ranged from .88 to .91 for the subscales. Conclusion: These results suggest that this needs assessment scale for nursing care in women with infertility demonstrated acceptable validity and reliability and contained items suitable for assessing the level of nursing care needed by women with infertility.

• Journal of The Geomorphological Association of Korea
• /
• v.21 no.4
• /
• pp.133-147
• /
• 2014

Reducing the range of target landform, is required to save the time and cost before real field survey in the case of inaccessible landform such as talus. In this study, Weight of Evidence modeling, which is a Target-driven spatial analysis statistics methods, has been applied to reduce the field survey range of target landform. In order to apply the Weight of Evidence analysis, a likelihood ratio was calculated on the basis of the result of correlation analysis between geomorphic factors and GIS information after selection of geomorphic factors regarding talus. A best combination, which has the biggest possibility for Talus Potential Index, was found by using SRC and AUC methods after calculating the number of cases for each thematic maps. This combination which includes aspect, geology, slope, land-cover, soil depth and soil drainage factors, showed quite high accuracy by 74.47% indicating the ratio of real existent talus to potential talus distribution.

• The Journal of the Korea Contents Association
• /
• v.19 no.1
• /
• pp.125-131
• /
• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• Journal of Ginseng Research
• /
• v.45 no.1
• /
• pp.58-65
• /
• 2021

Background: Panax ginseng, as one of the most widely used herbal medicines worldwide, has been studied comprehensively in terms of the chemical components and pharmacology. The proteins from ginseng are also of great importance for both nutrition value and the mechanism of secondary metabolites. However, the proteomic studies are less reported in the absence of the genome information. With the completion of ginseng genome sequencing, the proteome profiling has become available for the functional study of ginseng protein components. Methods: We optimized the protein extraction process systematically by using SDS-PAGE and one-dimensional liquid chromatography mass spectrometry. The extracted proteins were then analyzed by two-dimensional chromatography separation and cutting-edge mass spectrometry technique. Results: A total of 2,732 and 3,608 proteins were identified from ginseng root and cauline leaf, respectively, which was the largest data set reported so far. Only around 50% protein overlapped between the cauline leaf and root tissue parts because of the function assignment for plant growing. Further gene ontology and KEGG pathway revealed the distinguish difference between ginseng root and leaf, which accounts for the photosynthesis and metabolic process. With in-deep analysis of functional proteins related to ginsenoside synthesis, we interestingly found the cytochrome P450 and UDP-glycosyltransferase expression extensively in cauline leaf but not in the root, indicating that the post glucoside synthesis of ginsenosides might be carried out when growing and then transported to the root at withering. Conclusion: The systematically proteome analysis of Panax ginseng will provide us comprehensive understanding of ginsenoside synthesis and guidance for artificial cultivation.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.7
• /
• pp.29-36
• /
• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.