• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.12-22
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• 2018

In order to improve the vulnerability of current cities due to climate change, the disaster vulnerability analysis manual for various disasters is provided. Depending on the spatial units, the disaster vulnerability levels, and the conditions of the climatic factors, the results of the disaster vulnerability analysis will have a significant impact. In this study, relative assessments are conducted by adding the eup, myeon and dong unit in addition to census output area unit to analyze the impact on the spatial unit, and relative changes are analyzed according to the classification stages by expanding the natural classification, which is standardized at level four stage, to level two, four and six stage. The maximum rainfalls(10min, 60min, 24hr) are added for the two limited rainfall characteristics to determine the relativity of disaster vulnerable districts by index. The relative assessment results of heavy rainfall vulnerability index showed that the area ratio of disaster areas by spatial unit was different and the correlation analysis showed that the space analysis between the eup, myeon and dong unit in addition to census output area unit was not consistent. And it can be seen that the proportion of disaster vulnerable districts is relatively different a lot due to indexes of rainfall characteristics, spatial unit analysis and disaster vulnerability level stage. Based on the above results, it can be seen that the ratios of disaster vulnerable districts differ relatively significantly due to the level of the disaster vulnerability class, and the indexes of rainfall characteristics. This suggests that the impact of the disaster vulnerable districts depending on indexes is relatively large, and more detailed indexes should be selected when setting up the disaster vulnerabilities analysis index.

• Special Issue of the Society of Naval Architects of Korea
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• 2006.09a
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• pp.97-105
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• 2006

Until now, we are designing the breakwater of container vessel as cantilever structure ypically. Recently, we have designed "side shell touch type breakwater" for the first time to 6,200TEU Class Container Carrier registered on Lloyd Classification. The Lloyd Rule does not provide requirements for breakwater scantling but only recommend breakwater wave load and Lloyd Class requests for submitting the calculation results. At early design step, we had reviewed the breakwater structure through the calculation of simple beam theory with wave load recommended by Lloyd and referring to already built same size of container vessel. At the same time we had carried out F.E.M analysis of breakwater structure and had updated design, so we could verify the strength of side shell touch type breakwater at final step.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.337-351
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• 2016

As technologies' life-cycle shortens and their development directions are uncertain, firms' technology planning capability becomes increasingly important. Prior patent-based studies using technology growth curves identify developmental stages of technologies, thereby formulating technology development directions from an overall perspective. However, a technology generally consists of multiple sub-technologies and accordingly their development stages are likely various. In this regard, the prior studies failed to identify core sub-technologies and their specific development directions. Therefore, we suggest an approach consisting of 1) identifying core sub-technologies of a given technology using patent co-classifications and social network analysis, and 2) identifying each sub-technology's development stage and thereby determining its further development direction. We apply our approach to patents related to augmented reality to examine its applicability. It is expected that our approach will help identify evolving development stages for the core sub-technologies of a given technology, thereby effectively assisting technology experts in technology planning processes.

• Journal of Korean Society of Disaster and Security
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• v.10 no.1
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• pp.43-46
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• 2017

Recently, the development of underground space is being actively carried out in the urban area by saturation, and the excavation works are mainly carried out by various excavation methods by the structures adjacent to the ground and underground excavation. During such excavation work, ground subsidence accidents are occurring due to inattention construction, lack of construction technology, and leakage of ground water. For the prevention of ground subsidence we studied the method of risk influence factors by soil investigation. Analysis of 75 sites soil investigation by U.S.C.S (Unified Soil Classification System), construction method, depth of excavation and we studied the risk influence factors with ground subsidence.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.307-310
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• 2021

In the recent years, studies using Computer-Aided Diagnostics(CAD) have been actively conducted, such as signal and image processing technology using breast ultrasound images, automatic image optimization technology, and automatic detection and classification of breast masses. As computer diagnostic technology is developed, it is expected that early detection of cancer will proceed accurately and quickly, reducing health insurance and test ice for patients, and eliminating anxiety about biopsy. In this paper, a quantitative analysis of tumors was conducted in ultrasound images using a gray level co-occurrence matrix(GLCM) to experiment with the possibility of use for computer assistance diagnosis.

• Food Science of Animal Resources
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• v.38 no.2
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• Journal of Korean Library and Information Science Society
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• v.46 no.4
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• pp.191-205
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• 2015

As the first step in developing the optimal co-authorship allocation method, this study investigated the co-authorship allocation standards of Korean Universities on journal publications. The study compared the standards of 27 Korean universities with Library and Information Science (LIS) departments, and analyzed author rankings generated by applying inflated, fractional, harmonic, and university standard method of co-authorship allocation to 189 Korean LIS faculty publications from 2001 to 2014. The university standards most similar to the standard co-authorship allocation method in bibliometrics(i.e. Vinkler) were those whose co-author credits summed up to 1. However, the university standards differed from Vinkler's in allocating author credits based on primary and secondary author classification instead of allocation based on author ranks. The statistical analysis of author rankings showed that the harmonic method was most similar to the university standards. However, the correlation between the university standards whose co-author credits summed up to greater than 1 and harmonic method was lower. The study results also suggested that middle-level authors are most sensitive to co-authorship allocation methods. However, even the most generous university standards of co-authorship allocation still penalizes collaborative research by reducing each co-authors credit below those of single authors. Follow-up studies will be needed to investigate the optimal method of co-authorship credit allocation.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.626-629
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• 2004

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.22-30
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• 2001

In this paper we propose three classification algorithms to classify breast tumors that occur in duct into Benign, DCIS(ductal carcinoma in situ) NOS(invasive ductal carcinoma) The general approach for a creating classifier is composed of 2 steps: feature extraction and classification Above all feature extraction for a good classifier is very significance, because the classification performance depends on the extracted features, Therefore in the feature extraction step, we extracted morphology features describing the size of nuclei and texture features The internal structures of the tumor are reflected from wavelet transformed images with 10$\times$ and 40$\times$ magnification. Pariticulary to find the correlation between correct classification rates and wavelet depths we applied 1, 2, 3 and 4-level wavelet transforms to the images and extracted texture feature from the transformed images The morphology features used are area, perimeter, width of X axis width of Y axis and circularity The texture features used are entropy energy contrast and homogeneity. In the classification step, we created three classifiers from each of extracted features using discriminant analysis The first classifier was made by morphology features. The second and the third classifiers were made by texture features of wavelet transformed images with 10$\times$ and 40$\times$ magnification. Finally we analyzed and compared the correct classification rate of the three classifiers. In this study, we found that the best classifier was made by texture features of 3-level wavelet transformed images.

• Journal of Conservation Science
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• v.31 no.4
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• pp.395-402
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• 2015

This study was considered utilization of hyperspectral image analysis for monitoring. Accordingly we applied to stone cultural properties to data correction methods, image classification techniques, NDVI computation techniques using hyperspectral image. As the results, hyperspectral image analysis was possible making detailed deterioration map, accurate calculation of deterioration rate, mapping of normalized difference vegetation index on the basis of reflectance of each materials. Therefore, hyperspectral image analysis will be used for effective monitoring techniques of stone cultural heritages.