• Journal of the Korean Geographical Society
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• v.42 no.3 s.120
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• pp.355-367
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• 2007

As the global warming has been influenced on various sectors including agriculture, fisheries and health, it is essential to project more accurate future climate for an assessment of climate change impact and adaptation strategy. The purpose of this study is to examine the boundary changes in the subtropical climate region in South Korea using observed 30-year(1971-2000) data and projected 100-year data based on the IPCC SRES A1B emission scenario. We have selected Trewartha's climate classification among various climate classification, defining the subtropical climate region as the region with monthly mean temperature $10^{\circ}C$ or higher during 8-12 months. By observed data, the subtropical climate region was only limited in Jeju-do and the farmost southern coastal area(Busan, Tongyeong, Geoje, Yeosu, Wando, Mokpo) of South Korea. The future projected climate region for the period of 2071-2100 included have shown that subtropical climate region extended to most of stations except for the ares of Taebaeksan and Sobaeksan Mountains.

• Atmosphere
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• v.20 no.4
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• Smart Media Journal
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• v.10 no.2
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• pp.70-75
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• 2021

Grouping and classifying similar designs in design increase efficiency in terms of management and provide convenience in terms of use. Using artificial intelligence algorithms, this study attempted to classify textile designs into four categories: dots, flower patterns, stripes, and geometry. In particular, we explored whether it is possible to find and explain the regions of interest underlying classification from the perspective of artificial intelligence. We randomly extracted a total of 4,536 designs at a ratio of 8:2, comprising 3,629 for training and 907 for testing. The models used in the classification were VGG-16 and ResNet-34, both of which showed excellent classification performance with precision on flower pattern designs of 0.79%, 0.89% and recall of 0.95% and 0.38%. Analysis using the Local Interpretable Model-agnostic Explanation (LIME) technique has shown that geometry and flower-patterned designs derived shapes and petals from the region of interest on which classification was based.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.88.2-88.2
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• 2012

We investigate solar flare occurrence rate and daily flare probability depending on McIntosh sunspot classification, its area, and its area change. For this we use the NOAA active region and GOES solar flare data for 15 years (from January 1996 to December 2010). We consider the most flare-productive 10 sunspot classification: 'Dko', 'Dai', 'Eai', 'Fai', 'Dki', 'Dkc', 'Eki', 'Ekc', 'Fki', and 'Fkc'. Sunspot area and its change can be a proxy of magnetic flux and its emergence/cancellation, respectively. we classify each sunspot group into two sub-groups: 'Large' and 'Small'. In addition, for each group, we classify it into three sub-groups according to sunspot group area change: 'Decrease', 'Steady', and 'Increase'. As a result, in the case of compact groups, their flare occurrence rates and daily flare probabilities noticeably increase with sunspot group area. We also find that the flare occurrence rates and daily flare probabilities for the 'Increase' sub-groups are noticeably higher than those for the other sub-groups. In case of the (M+X)-class flares of 'Dkc' group, the flare occurrence rate of the 'Increase' sub-group is three times higher than that of the 'Steady' sub-group. Mean flare occurrence rates and flare probabilities for all sunspot regions increase with the following order: 'Steady', 'Decrease', and 'Increase'. Our results statistically demonstrate that magnetic flux and its emergence enhance major solar flare occurrence. We are going to forecast solar flares based on these results and NOAA scale.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.610-614
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• 2005

Rock mass classification by using electrical resistivity tomography(ERT) method is widely performed for the determination of rock support type in tunnel design. In the region of high electrical noise level, however, the result of the ERT will have many erroneous features. In this study, the back ground electrical noise had been measured to find out the reason why the results of ERT in this area did not agree to the expected geology confirmed by boreholes. In order to overcome this limitation of ERT, a hybrid surface-borehole array seismic refraction tomography had been followed. Using this technique, we could get P-wave velocity section including the depth level of tunnel. The comparison of the P-wave velocity and RMR shows fairly good statistical relationship to make it possible to set up the rock mass classification for the entire tunnel line.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.144-147
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• 1999

The objective of this study is to quantitatively evaluate the effects of various SAR speckle reduction methods for multisource land-cover classification by backpropagation neural network, especially over the coastal region. The land-cover classification using neural network has an advantage over conventional statistical approaches in that it is distribution-free and no prior knowledge of the statistical distributions of the classes is needed. The goal of multisource land-cover classification acquired by different sensors is to reduce the classification error, and consequently SAR can be utilized an complementary tool to optical sensors. SAR speckle is, however, an serious limiting factor when it is exploited for land-cover classification. In order to reduce this problem. we test various speckle methods including Frost, Median, Kuan and EPOS. Interpreting the weights about training pixel samples, the “Importance Value” of each SAR images that reduced speckle can be estimated based on its contribution to the classification. In this study, the “Importance Value” is used as a criterion of the effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.398-405
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• 2006

We propose a pattern classification model using a selective perception ability of human beings. Generally, human beings recognize an object by putting a selective concentration on it in the region of interest. Much better classification and recognition could be possible by adapting this phenomenon in pattern classification. First, the pattern classification model creates some reference cluster patterns in a usual way. Then it generates an SPM(Selective Perception Map) that reflects the mutual relation of the reference cluster patterns. In the recognition phase, the model applies the SPM as a weight for calculating the distance between an input pattern and the reference patterns. Our experiments show that the proposed classifier with the SPM acquired the better results than other approaches in pattern classification.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.633-636
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• 2004

Airborne laser scanning thechnology has been studied in many applications, DSM(Digital Surface Model) development, building extraction, 3D virtual city modeling. In this paper, we will evaluate the possibility of airborne laser scanning technology for transportation application, especially for recognizing moving vehicles on road. First, we initially segment the region of roads from all LiDAR DATA using the GIS map and intensity image. Secondly, the segmented region is divided into the roads and vehicles using the height threshold value of local based window. Finally, the vehicles will be classified into the several types of vehicles by MDC(Minimum Distance Classification) method using the vehicle's geometry information, height, length, width, etc