• Journal of Plant Biotechnology
• /
• v.42 no.4
• /
• pp.326-335
• /
• 2015

Citrus is an economically important fruit tree with the largest amount of fruit production in the world. It provides important nutrition such as vitamin C and other health-promoting compounds including its unique flavonoids for human health. However, it is classified into the most difficult crops to develop new cultivars through conventional breeding approaches due to its long juvenility and some unique reproductive biological features such as gamete sterility, nucellar embryony, and high level of heterozygosity. Due to global warming and changes in consumer trends, establishing a systematic and efficient breeding programs is highly required for sustainable production of high quality fruits and diversification of cultivars. Recently, reference genome sequences of sweet orange and clementine mandarin have been released. Based on the reference whole-genome sequences, comparative genomics, reference-guided resequencing, and genotyping-by-sequencing for various citrus cultivars and crosses could be performed for the advance of functional genomics and development of traits-related molecular markers. In addition, a full understanding of gene function and gene co-expression networks can be provided through combined analysis of various transcriptome data. Analytic information on whole-genome and transcriptome will provide massive data on polymorphic molecular markers such as SNP, INDEL, and SSR, suggesting that it is possible to construct integrated maps and high-density genetic maps as well as physical maps. In the near future, integrated maps will be useful for map-based precise cloning of genes that are specific to citrus with major agronomic traits to facilitate rapid and efficient marker-assisted selection.

• Korean Journal of Remote Sensing
• /
• v.32 no.2
• /
• pp.171-183
• /
• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• Korean Journal of Remote Sensing
• /
• v.21 no.3
• /
• pp.189-211
• /
• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.2
• /
• pp.102-116
• /
• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.9 no.1
• /
• pp.89-100
• /
• 2006

Recently the dramatical development of domestic spatial information technology and the successful construction of Korea NGIS(Nation Geographic Information System) have been the foundation of the scientific national territory utilization and management. However, there still exists the original problems to construct the thematic maps for supporting the various administrative services because administrative officials tend to depend on paper maps and inventories to generate spatial information, process, upgrade and manage. In this situation, there is a greater need to develop GIS system for the effective construction of various thematic maps. In this study, the satellite image based high accurate e-thematic construction and management system was developed to support the forest administrative service such as generating user based forest thematic maps, modifying them, analyzing and outputting through GIS, GPS and satellite images. For the case study, the previous forest paper map of Jeju Island was converted in format of raster and vector data using satellite images to maintain more exact location information so that this system helps to manage domestic spatial information scientifically and effectively within shorter time then support the standard for domestic spatial information. Moreover, this system plays the role of DSS(Decision Supporting System) for forest administrative affairs by integrating the attribute data, managing the GPS data and linking the multimedia data. For this, the additional main objective of this study was acquired powerful GIS component, which is called as e-mapping component, so that it could be regarded as enabling interoperability and reusability within this application. For the future works, the essential element idea and technology in this study could be applied very usefully to other official works such as constructing thematic maps and supporting the desired affairs.

• Journal of Korean Academy of Nursing Administration
• /
• v.1 no.1
• /
• pp.22-34
• /
• 1995

Health care marketing can be defined as a process of developing, promoting and administering health care services for the benefits of hospitals as well as consumers. Researchers have reported that one of the significant attributes in the selection of hospital is a client's perceived image of the hospitals. Especially the image of nursing services was one of the important factors of the hospital image. The main purposes of the study were 1) to define the positioning about 5 hospitals' image of nursing services. 2) to define the effects of image attributes to the iamage of hospital nursing services. Eighteen hundred questionnaires were distributed to the parents of middle schools and high schools in Seoul. Among them, 1340 cases were returned but 849 cases were turned out to be useful data and used in final analysis. In data analysis, $SPSS\;/PC^+$ and PC-MDS programs were used for descriptive statistics, multiple regression, and drawing perceptual maps. The image of hospital nursing services was measured developed by the author through literature review and content validation. Reliability coefficients was found to be statistically appropriate level of confidence(Cronbach's Alpha=.8617). The results were as follows : 3) The perceptual map shows that hospital A, B and C,D,E were divided into five groups(See Fig. 1). That is, hospital A, and B are located in a close position and hopital C & E are located at opposite position between X-axis, Hospital D was located in the middle area of the hospital C and:E. In conclusion, this research visiblely depicted perceptual maps using MDS of the consumer's image about hospital nursing services. Since hopital nursing services were differentiated by the image attributes in consumer's perception, the results of the study can help hospital managers plan marketing strategy according to their strong points or weak points. Because the necessity of marketing in recent health care was importantly recognized, this research which is adopting posititioning concept will contribute to the consumers and hospital organizations.

• Journal of the Korean Mathematical Society
• /
• v.51 no.2
• /
• pp.345-362
• /
• 2014

We describe the Haagerup tensor product ${\ell}^{\infty}{\otimes}_h{\ell}^{\infty}$ and the extended Haagerup tensor product ${\ell}^{\infty}{\otimes}_{eh}{\ell}^{\infty}$ in terms of Schur product maps, and show that ${\ell}^{\infty}{\otimes}_h{\ell}^{\infty}{\cap}\mathbb{B}({\ell}^2)$(resp. ${\ell}^{\infty}{\otimes}_{eh}{\ell}^{\infty}{\cap}\mathbb{B}({\ell}^2)$) coincides with $c_0{\otimes}_hc_0{\cap}\mathbb{B}({\ell}^2)$(resp. $c_0{\otimes}_{eh}c_0{\cap}\mathbb{B}({\ell}^2)$). For $C^*2$-algebras A, B, it is shown that $A{\otimes}_hB=A{\otimes}_{eh}B$ if and only if A or B is finite-dimensional.

• Kyungpook Mathematical Journal
• /
• v.58 no.4
• /
• pp.589-597
• /
• 2018

For $A,B{\in}M_2(\mathbb{C})$, let the Jordan product be AB + BA and G(A) the eigenvalue inclusion set, the Gershgorin set of A. Characterization is obtained for maps ${\phi}:M_2(\mathbb{C}){\rightarrow}M_2(\mathbb{C})$ satisfying $$G[{\phi}(A){\phi}(B)+{\phi}(B){\phi}(A)]=G(AB+BA)$$ for all matrices A and B. In fact, it is shown that such a map has the form ${\phi}(A)={\pm}(PD)A(PD)^{-1}$, where P is a permutation matrix and D is a unitary diagonal matrix in $M_2(\mathbb{C})$.

• Bulletin of the Korean Mathematical Society
• /
• v.56 no.4
• /
• pp.885-898
• /
• 2019

This paper gives a counterexample to show that the first order commutator $C_2$ is not bounded from $H^1({\mathbb{R}}){\times}H^1({\mathbb{R}})$ into $L^{1/2}({\mathbb{R}})$. Then we introduce the atomic definition of abstract weighted Hardy spaces $H^1_{ato,{\omega}}$$({\mathbb{R}})$ and study its properties. At last, we prove that $C_2$ maps $H^1_{ato,{\omega}}$$({\mathbb{R}}){\times}H^1_{ato,{\omega}}$$({\mathbb{R}})$ into $L^{1/2}_{\omega}$$({\mathbb{R}})$.

• The KIPS Transactions:PartC
• /
• v.12C no.5 s.101
• /
• pp.649-658
• /
• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.