• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.495-506
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• 2018

Global interest in climate change and sea level rise has led to active research on the velocities of glaciers. In studies about the velocity of glaciers, in-situ measurements can obtain the most accurate data but have limitations to acquire periodical or long-term data. Offset tracking using SAR is actively being used as an alternative of in-situ measurements. Offset tracking has a limitation in that the accuracy of observation is lower than that of other observational techniques, but it has been improved by recent studies. Recent studies in the $Uv{\hat{e}}rsbreen$ glacier area have shown that glacier altitudes decrease at a rate of 1.5 m/year. The glacier displacement velocities in this region are heavily influenced by climate change and can be important in monitoring and forecasting long-term climate change. However, there are few concrete examples of research in this area. In this study, we applied the improved offset tracking method to observe the two-dimensional velocity in the $Uv{\hat{e}}rsbreen$ glacier. As a result, it was confirmed that the glacier moved at a maximum rate of 133.7 m/year. The measruement precisions for azimuth and line-of-sight directions were 5.4 and 3.3 m/year respectively. These results will be utilized to study long-term changes in elevation of glaciers and to study environmental impacts due to climate change.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.823-833
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• 2020

In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.473-493
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• 2012

Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.2
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• pp.69-89
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• 2020

Scientific trust and quantification of traditional species investigation and results that have been used in ecology for decades has always been a problem and concern for ecologists. Global ecologists have proposed DNA-based species investigation studies to find answers to problems. In this study, we reviewed the global trend of research on environmental DNA(eDNA), which is a method for monitoring species by detecting DNA of organisms naturally mixed in environmental samples such as water, soil, and feces. The first eDNA research confirmed the possibility of species investigation at the molecular level, and commercialization of NGS(Next Generation Sequencing) and DNA metabarcoding elicits efficient and quantitative species investigation results, and eDNA research is increasing in the filed of ecology. In this study, mammals and birds were detected using MiMammal universal primers from 23 samples(3 natural reserves; 20 water bowls) out of 4 patches to verify eDNA for urban ecosystems in Suwon, and eDNA was verified by performing camera trapping and field survey. Most terrestrial species were detected through eDNA, and particularly, mice(Mus musculus), and Vinous-throated Parrotbill (Sinosuthora webbiana) were identified only with eDNA, It has been confirmed to be highly effective by investigating techniques for small and internal species. However, due to the lack of resolution of the primer, weasels(Mustela sibirica) and squirrels(Melanochromis auratus) were not detected, and it was confirmed that the traditional investigation method was effective only for a few species, such as Mogera robusta(Mogera robusta). Therefore, it is judged that the effects of species investigation can be maximized only when eDNA is combined with traditional field survey and Camera trapping to complement each other.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1367-1383
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• 2007

A sensor network consists of a network of sensors that can perform computation and also communicate with each other through wireless communication. Some important characteristics of sensor networks are that the network should be self administered and the power efficiency should be greatly considered due to the fact that it uses battery power. In sensor networks, when large amounts of various stream data is produced and multiple queries need to be processed simultaneously, the power efficiency should be maximized. This work proposes a technique to create an index on multiple monitoring queries so that the multi-query processing performance could be increased and the memory and power could be efficiently used. The proposed SMILE tree modifies and combines the ideas of spatial indexing techniques such as k-d trees and R+-trees. The k-d tree can divide the dimensions at each level, while the R+-tree improves the R-tree by dividing the space into a hierarchical manner and reduces the overlapping areas. By applying the SMILE tree on multiple queries and using it on stream data in sensor networks, the response time for finding an indexed query takes in some cases 50% of the time taken for a linear search to find the query.

• Journal of the Korean Vacuum Society
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• v.15 no.4
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• pp.421-426
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• 2006

In examining particulate deposits in the pipes of a chemical vapor deposition (CVD) system, vibration diagnostics is compared and studied against ultrasonic diagnostics, The latter method involves pulsing the outer wall of pipes with an ultrasonic sensor and analyzing the resulting echo to observe particulate deposits inside pipes. Vibration diagnostics examines the existence of particulate deposits by analyzing the difference in the frequencies generated when a vibrator is adhered to the outer wall of pipes. With ultrasonic diagnostics, good test results were obtained only when particulate deposits were attached to the inner wall of the pipes, After some time, however, particulate deposits were not detected properly, as the ultrasonic wave failed to cross the fine gaps created between the inner wall of the pipe and the deposits. The ultrasonic wave bounced back because of the dried particulate deposits on the wall. Thus, it has been proven that the ultrasonic diagnostics is not an appropriate means of examining the particulate deposits in a vacuum, On the other hand, vibration diagnostics succeeded in detecting the particulate deposits regardless of the lapsed time. In conclusion, the vibration diagnostics is being expected as the effective method in monitoring the particulate deposits inside pipes in the CVD system where the desired behavior is reduced frequency along with the particulate deposits in comparison to the case where the pipe is clean.

• Food Science and Preservation
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• v.19 no.1
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• pp.62-66
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• 2012

With the rapid growth of fresh-cut produce market, the South Korean fresh-cut industry is facing the challenge of ensuring food safety. As the estimation of the microbial numbers in fresh-cut produce processing lines (tools, and equipment) using the conventional microbiological techniques takes days, so there is a need for faster and easier monitoring methods. This study was conducted to investigate the use of ATP bioluminescence assay to measure the degree of microbial contamination from three actual fresh-cut processing lines. The samples collected from frech-cut vegetables, and fresh-cut fruits processing plants were tested for the estimation of the bacterial number, using the ATP bioluminescence and microbiological methods. The result of former was transferred to log RLU/100 $cm^2$, and that of the latter was transferred to log CFU/100 $cm^2$. A positive linear correlation between the ATP bioluminescence assay value and aerobic-plate count was found for fresh-cut processing lines, with a correlation coefficient of 0.8772 (n=50). The results of this study indicate that ATP bioluminescence assay can be used to monitor microbial contamination in fresh-cut produce processing plants, and can help improve the hygiene therein.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.3
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• pp.357-374
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• 2000

The purpose of this study was to validate knowledge outcomes included Nursing Outcomes Classification(NOC) developed by Johnson and Maas at the University of Iowa. A sample of 71 nurse experts working in university affiliated hospitals participated in this study. They were asked to rate indicators that examplified the outcomes on a scale of 1(indicator is not all characteristic) to 5(indicator is very characteristic). A questionnaire with an adaptation of Fehring's methodology was used to establish the content validity of outcomes. The results were as follow: 1. All indicators were considered to be 'supporting' and no indicators were considered to be 'nonsupporting'. 2. 'Knowledge: Treatment Regimen' attained and OCV score of 0.816 and was the highest OCV score among outcomes. 3. 'Knowledge: Energy Conservation' attained an OCV score of 0.748 and was the lowest OCV score among abuse outcomes. 4. 'Knowledge: Breastfeeding' attained an OCV score of 0.790 and was the highest indicator was 'description of benefits of breastfeeding'. 5. 'Knowledge: Child Safety' attained an OCV score of 0.778 and was the highest indicator was 'demonstration of first aids techniques'. 6. 'Knowledge: Diet' attained an OCV score of 0.779 and was the highest indicator was 'performance of self-monitoring activities'. 7. 'Knowledge: Disease Process' attained an OCV score of 0.815 and was the highest indicator was 'description of signs and symptoms'. 8. 'Knowledge: Health Behaviors' attained an OCV score of 0.800 and was the highest indicator was 'description of safe use of prescription drugs'. 9. 'Knowledge: Health Resources' attained an OCV score of 0.794 and was the highest indicator was 'description of need for follow-up care'. 10. 'Knowledge: Infection Control' attained an OCV score of 0.793 and was the highest indicator was 'description of signs and symptoms'. 11. 'Knowledge: Medication' attained an OCV score of 0.789 and was the highest indicator was 'description of correct administration of medication'. 12. 'Knowledge: Personal Safety' attained an OCV score of 0.804 and was the highest indicator was 'description of measures to reduce risk of accidental injury'. 13. 'Knowledge: Prescribed Activity' attained an OCV score of 0.810 and was the highest indicator was 'proper performance of exercise'. 14. 'Knowledge: Substance Use Control' attained an OCV score of 0.809 and was the highest indicator was 'description of signs of dependence during substance withdrawl'. 15. 'Knowledge: Treatment Procedure(s)' attained an OCV score of 0.795 and was the highest indicator was 'description of appropriate action for complications'. 16. 'Knowledge: Treatment Regimen' attained an OCV score of 0.816 and was the highest indicator was 'description of self-care responsibilities for emergency situations'. More outcomes need to be validated and outcomes sensitive to Korean culture need to be developed.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.145-167
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• 2013

This paper proposes a method to compare planned processes with actual processes of bock assembly operations in shipbuilding industry. Process models can be discovered using the process mining techniques both for planned and actual log data. The comparison between planned and actual process is focused in this paper. The analysis procedure consists of five steps : 1) data pre-processing, 2) definition of analysis level, 3) clustering of assembly bocks, 4) discovery of process model per cluster, and 5) comparison between planned and actual processes per cluster. In step 5, it is proposed to compare those processes by the several perspectives such as process model, task, process instance and fitness. For each perspective, we also defined comparison factors. Especially, in the fitness perspective, cross fitness is proposed and analyzed by the quantity of fitness between the discovered process model by own data and the other data(for example, the fitness of planned model to actual data, and the fitness of actual model to planned data). The effectiveness of the proposed methods was verified in a case study using planned data of block assembly planning system (BAPS) and actual data generated from block assembly monitoring system (BAMS) of a top ranked shipbuilding company in Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.