• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.125-145
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• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.597-604
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• 2001

The objective of the paper was to estimate the changes in acreages of tidal flats after the seawall construction at the Asan Bay and the Chunsu Bay from topographic maps, hydrographic charts, and Landsat TM images. The tidal floats from topographic maps published in one year differ significantly from that in the other, which appears to be attributed to the tide levels at the time of photographing. The hydrographic charts showed that tidal flats increase at rates of 22.3 ha/yr at the Asan Bay and 56.6 ha/yr at the Chunsu Bay after the dike construction. Applying the ISODATA method of unsupervised classifications for the Landsat TM images, the tidal flats were identified, and the resulting acreages for each image estimated. The resulting tidal flats increased at the rates of 21.3 ha/yr at the Asan Bay and 47.3 ha/yr at the Chunsu Bay during twelve years after the dike construction. It was found that the rates of the annual increases from the two data are very close and the differences result from the coastal lines at the charts and the TM images.

• Journal of Korea Water Resources Association
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• v.50 no.6
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• pp.429-440
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• 2017

A series of multispectral high-resolution Korean Multi-Purpose Satellite (KOMPSAT) images were analyzed to detect the geographical changes in four different tidal flats in the west coast of South Korea. The method of unsupervised classification was used to generate a series of land use/land cover (LULC) maps from the satellite images, which were used as the input of the temporal trajectory analysis to detect the temporal change of coastal wetlands and its association with natural and anthropogenic activities. The accurately classified LULC maps extracted from the KOMPSAT images indicate that these multispectral high-resolution satellite data is highly applicable to generate good quality thematic maps for extracting wetlands. The result of the trajectory analysis showed that, while the tidal flat area of Gyeonggi and Jeollabuk provinces was estimated to have changed due to tidal effects, the reductive trajectory of the wetland areas belonging to the Saemangeum province was caused by a high degree of human-induced activities including large reclamation and urbanization. The conservation of the Jeungdo Wetland Protected Area in Jeollanam province revealed that the social and environmental policies can effectively protect coastal wetlands from degradation. Therefore, monitoring for wetland change using high resolution KOMPSAT is expected to be useful to coastal environment management and policy making.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.1-15
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• 1999

This study was carried out to find out the way to build a comprehensive wetland ecosystem database using the technique of remote sensing and geographic information system. A Landsat TM image taken in May 17, 1997 was used for the primary source for the image analysis. Field surveys were conducted March to September of 1998 to help image analysis and examine the results. An actual wetland vegetation map was created based on the field survey. A Landsat TM image was analyzed by unsupervised and supervised classification methods and finally categorized into such 5 classes as Phragmites australis community, mixed community, sand beach, Scirpus trigueter community and non-vegetation intertidal area. Wetland basemap was developed for the overall accuracy assesment in wetland mapping. Vegetation index map of wetland vegetation was developed using NDVI(normalized difference vegetation index). The map of wetland productivity was developed based on the productivity of Phragmites australis and the relationship to the proximity of adjacent water bodies. The map of potential vegetation succession map was also developed based on the experience and knowledge of the field biologists. Considering these results, it is possible to use the remote sensing and GIS techniques for producing wetland ecosystem database. This study indicated that these techniques are very effective for the development of the national wetland inventory in Korea.

• The Plant Pathology Journal
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• v.34 no.5
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• pp.381-392
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• 2018

Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in $M{\acute{e}}xico$. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy ($532nm\;{\lambda}$ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants ${\times}$ 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and $1510cm^{-1}$ were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).

• Korean Journal of Ecology and Environment
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• v.36 no.1 s.102
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• pp.29-37
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• 2003

Benthic macro-invertebrates were seasonally collected in the Onchen Stream in Pusan, from July 2001 to March 2002. Generally 4 phylum 5 class 10 order 19 family 23 species were observed in the study sites. Ephemeroptera, Plecoptera and various species appeared in headwater stream while Oligochaeta and Chironomidae were dominated in downstream sites. Community abundance patterns, especially the dominant taxa, Oligochaeta and Chironomidae, appeared to be different depending upon the sampling months. Oligochaeta was usually observed in July, December and March while Chironomidae was appeared in September. The biological indices, TBI(Trent Biotic Index), BS (Biotic Score), BMWP (Biological Monitoring Working Party)were calculated with the appeared communities of the sampling sites through the survey months. TBI showed 1 to 8, BMWP was 1 to 93 and CBI appeared 9 to 387 in the different sites. The biological indices decreased from headstream to downstream sites, We implemented the unsupervised Kohonen network for patterning of community abundance of the sampling sites. The patterning map by the Kohonen network was well represented community abundance of the sampling sites. Also, we conducted RTRN (Real Time Recurrent Neural Network) for predicting of the biological indices in the different sites. The results appeared that the predicting values by RTRN were well matched field data (correlation coefficient of TBI, BMWP and CBI were 0.957, 0.979 and 0.967, respectively).

• Journal of Life Science
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• v.14 no.5
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• pp.874-881
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• 2004

Angiogenesis has been implicated in progression of inflammation, arthritis, psoriasis, atherosclerosis as well as tumor growth and metastasis. Intensive studies have been carried out to develop a strategy for cancer treatment by blocking angiogenesis. During angiogenesis, endothelial proliferation and migration essentially occurs upon activation. In this study, we compared the expression profiles of human umbilical endothelial cells activated by incubating in vitro in the rich medium containing several growth factors, and non-activated ones. cDNA targets derived from total RNAs of HUVEC activated for 13 h in M199 medium containing endothelial cell growth supplement, 20% fetal bovine serum, and heparin, after reaching 70～80% confluency, or non-activated, were hybridized onto oligonucleotide microarrays containing 1,8864 genetic elements. Unsupervised hierarchical clustering analysis resulted in two subgroups on dendrogram exhibiting activated and non-activated HUVECs. We then extracted 122 outlier genes which were shown to be up-regulated or under-expressed by at least 2-folds in activated HUVECs. Among these, 32 annotated genes were up-regulated and 38 were down-regulated in activated HUVECs. Interestingly, genes involved in cell proliferation, motility, and inflammation/ immune response were up-regulated in activated HUVEC, whereas genes for cell adhesion or vessel morphogenesis/function were down-regulated. Unexpectedly, the expression of genes well-characterized as angiogenesis markers was not changed except Eph-B4, which was down-regulated about 4 folds. 52 unknown genes were also up- or down-regulated. Therefore, these results could provide an opportunity to targeting new vascular molecules for the development of anti-angiogenic molecules.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.63-69
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• 2013

Change detection technique is essential to various applications of Very High-Resolution(VHR) satellite imagery and land monitoring. However, change detection accuracy of VHR satellite imagery can be decreased due to various geometrical dissimilarity. In this paper, the existing fusion evaluation indexes were revised and applied to improve VHR imagery based change detection accuracy between multi-temporal images. In addition, appropriate change detection methodology of VHR images are proposed through comparison of general change detection algorithm with cross-sharpened image based change detection algorithm. For these purpose, ERGAS, UIQI and SAM, which were representative fusion evaluation index, were applied to unsupervised change detection, and then, these were compared with CVA based change detection result. Methodologies for minimizing the geometrical error of change detection algorithm are analyzed through evaluation of change detection accuracy corresponding to image fusion method, also. The experimental results are shown that change detection accuracy based on ERGAS index by using cross-sharpened images is higher than these based on other estimation index by using general fused image.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.769-783
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• 2000

In this paper, in order to disambiguate Korean noun word sense, we define a local context and explain how to extract it from a raw corpus. Following the intuition that two different nouns are likely to have similar meanings if they occur in the same local context, we use, as a clue, the word that occurs in the same local context where the target noun occurs. This method increases the usability of extracted knowledge and makes it possible to disambiguate the sense of infrequent words. And we can overcome the data sparseness problem by extending the verbs in a local context. The sense of a target noun is decided by the maximum similarity to the clues learned previously. The similarity between two words is computed by their concept distance in the sense hierarchy borrowed from WordNet. By reducing the multiplicity of clues gradually in the process of computing maximum similarity, we can speed up for next time calculation. When a target noun has more than two local contexts, we assign a weight according to the type of each local context to implement the differences according to the strength of semantic restriction of local contexts. As another knowledge source, we get a co-occurrence information from dictionary definitions and example sentences about the target noun. This is used to support local contexts and helps to select the most appropriate sense of the target noun. Through experiments using the proposed method, we discovered that the applicability of local contexts is very high and the co-occurrence information can supplement the local context for the precision. In spite of the high multiplicity of the target nouns used in our experiments, we can achieve higher performance (89.8%) than the supervised methods which use a sense-tagged corpus.