• The Korean Journal of Nuclear Medicine
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• v.36 no.6
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• pp.368-380
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• 2002

Purpose: Liver transplantation (LT), one of the therapeutic options of primary liver cancer has been suffering from recurrence caused by metastasis in 8-54% of patients. This study was performed to investigate whether FDG-PET is useful for detecting hidden metastasis in LT candidates. Materials and Methods: Twenty-six patients (male:female=23:3, mean age 55.7 years) underwent FDG-PET. Their previous conventional diagnostic studies (CDS) like abdomen US and CT, chest x-ray and CT, and bone scan were negative (n=22) or equivocal (n=4) for metastasis. Positive FDG-PET findings were confirmed by biopsy or clinical follow-up. Results: Among 4 patients with equivocal metastatic lesions on CDS, 3 had 6 hypermetabolic lesions on FDG-PET, which were confirmed as metastasis and subsequently LTs were cancelled. Of these, 5 lesions were initially negative on CDS. Remained 1 patient underwent LT with a negative FDG-PET result. Among 22 patients without metastasis on CDS, 5 had 7 hypermetabolic lesions on FDG-PET. One of these patients proved to have 2 metastatic lesions, and LT was cancelled. The other 4 patients had S hypermetabolic lesions on FDG-PET, which were confirmed as benign lesions, and 3 patients of them underwent LT. In summary, FDG-PET was useful in avoiding 4 unwarranted LT by detecting unsuspected metastatic lesions on CDS. A total of 17 patients underwent LT. In comparison with pathology, the sensitivity and specificity of FDG-PET for detecting viable primary liver cancer were 55.6% (5/9) and 87.5% (7/8), respectively. Conclusion: FDG-PET can detect additional hidden metastasis and contribute to reducing unwarranted LT in the patients with primary liver cancer.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• Journal of Christian Education in Korea
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• v.66
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• pp.343-382
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• 2021

This study is a case study in which the interactive non-face-to-face classes using Edutech were applied to the Department of Christian Education. The subjects were 20 students from the Christian education department of A University located in the metropolitan area. The course was 'Instructional Methods and Educational Technology' in the first semester of 2020. In theory, I studied non-face-to-face classes and interaction, and edutech and interaction. Afterward, it designed and developed interactive non-face-to-face classes using edutech. The interactive non-face-to-face classes using edutech were developed as a process of applying Flipped-PBL based interactive edutech. In addition, Edutech was selected for active interaction according to the Flipped-PBL process to be carried out in a non-face-to-face situation. In particular, in the process of developing the problem of PBL, it was built around the situation of the church. As a result of applying the class, first, learners showed high satisfaction and interest in the class. Second, positive transference appeared in the space of learning and the space of living. Third, interactive non-face-to-face classes using Edutech have generated active interaction. In particular, interactive edutech and learning methods have become the main factors enabling active interaction. Through this, learners have improved learning efficiency, immersion, and satisfaction. Also, as an alternative to face-to-face classes, I was able to experience online classes. In other words, the satisfaction and interest of learning, and the transference of learning space, were also possible through active interactions generated through learning methods using interactive Edutech used in class. Furthermore, disabilities in the online communication(Internet) environment and learners' unfamiliarity with the online environment have been found as factors that hinder learning satisfaction and interaction. During learning, obstacles to the online communication environment hinder the utilization of interactive Edutech, preventing active interactions from occurring. This results in diminishing satisfaction and interest in learning. Therefore, we find that designing interactive non-face-to-face classes using Edutech requires sufficient learner learning and checking of the online communication(Internet) environment in advance for Edutech and learning methods. In response, this study confirmed the possibility by applying interactive non-face-to-face classes using Edutech to Christian education classes as an alternative method of education that allows active interaction and consistent transference of learning and life. Although it is a case study with limited duration and limitations of the number of people, I would like to present the possibility as an alternative Christian education method of an era where the direction of online classes should be presented as an alternative to a face-to-face class.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.19-25
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• 2019

The CleanSYS(Clean SYStem) is operated to monitor air pollutants emitted from specific industrial complexes in Korea. So the industrial complexes without the system are directly monitored by the control officers. For efficient monitoring, studies using various sensors have been conducted to monitor air pollutants emitted from industrial complex. In this study, hyperspectral sensors were used to model and verify the equations for estimating the concentration of $NO_2$(nitrogen dioxide) in air pollutants emitted. For development of the equations, spectral radiance were observed for $NO_2$ at various concentrations with different SZA(Solar Zenith Angle), VZA(Viewing Zenith Angle), and RAA(Relative Azimuth Angle). From the observed spectral radiance, the calculated value of the difference between the values of the specific wavelengths was taken as an absorption depth, and the equations were developed using the relationship between the depth and the $NO_2$ concentration. The spectral radiance mixed gas of $NO_2$ and $SO_2$(sulfur dioxide) was used to verify the equations. As a result, the $R^2$(coefficient of determination) and RMSE(Root Mean Square Error) were different from 0.71~0.88 and 72~23 ppm according to the form of the equation, and $R^2$ of the exponential form was the highest among the equations. Depending on the type of the equations, the accuracy of the estimated concentration with varying concentrations is not constant. However, if the equations are advanced in the future, hyperspectral sensors can be used to monitor the $NO_2$ emitted from the industrial complex.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.101-116
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• 2019

Technology-based start-up is important in that it encourages innovation, facilitates the development of new products and services, and contributes to job creation. Technology-based start-up activates entrepreneurship when appropriate support is provided within the ecosystem. Thus, understanding the technology-based start-up ecosystem is crucial. The purpose of this study is as follows. First, in Herrmann et al.'s(2015) study, we compare and analyze the ecosystem of each country by selecting representative regions such as Silicon Valley, Tel Aviv, London and Singapore which have the highest ranking in the start-up ecosystem. Second, we try to deeply understand the start-up ecosystem based on in-depth interviews with various stakeholders such as VC investors, start-ups, support organizations, and professors related to the Korean start-up ecosystem. Finally, based on the results of the study, we suggest development and activation of Korean technology-based start-up ecosystem. As a result, the Seoul start-up ecosystem showed a positive evaluation of government support compared to other advanced countries. In addition, it was confirmed that the ratio of tele-work and start-up company working experience of employees was higher than other countries. On the other hand, in Seoul, It was confirmed that overseas market performance, human resource diversity, attracting investment, hiring technological engineers, and the ratio of female entrepreneurs were lower than those of overseas advanced countries. In addition, according to the results of the interview analysis, Seoul was able to find that start-up ecosystems such as individual angel investors, accelerators, support institution, and media are developing thanks to the government's market-oriented policy support. However, in order for this development to continue, it is necessary to improve the continuous investment system, expansion of diversity, investment return system, and accessibility to the global market. A discussion on this issue is presented.

• Journal of vocational education research
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• v.37 no.2
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• pp.39-75
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• 2018

The purpose of this study is to verify the general characteristics in the previous studies and the magnitude of the correlation between the learner's satisfaction and the academic achievement in the education and training program. To do this, we searched relevant literature from 2000 to 2016, and conducted a systematic review of the literature on the final 31 studies through the selection criteria and quality evaluation. Among them, 27 meta-analysis of the literature was conducted. The finding of the study were as follows. First, a total of 31 studies were conducted from 2000 to 2016, and more than half of them(16) were conducted for the last 4 years(2009~2012). In terms of education and training students, there are 18 college students, 9 workers, and 4 elementary students in order of study. In terms of methods, 15 collective education, 14 distance education, 2 blended education. In terms of learner's participation, 22 the general participation, 9 the active participation. Second, as a result of the meta-analysis, the magnitude of the correlation between satisfaction and achievement was moderate(ZCOR=.297, 95%: CI .210~.383). Third, as a result of verifying the difference in the magnitude of the correlation effect between satisfaction and achievement according to the characteristics of the education and training program, there was no difference between the groups in the student object and education method, but there was a difference in the magnitude of the correlation effect depending on the participant type(Q=15.40, df=1, p<.0001). The active participation showed a correlation effect size larger(ZCOR=.588, 95%: CI .422~.754). The effect size of the general participation was lower than the median(ZCOR=.211, 95%: CI .12 ~.300).