• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1373-1387
• /
• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.37 no.2
• /
• pp.30-39
• /
• 2019

In this study, 27 representative plants with symbolism and appreciation were extracted from 68 plants collected in Li Yu (1611-1680)'s monograph named 'Xianqingouji Zhongzhibu'. The interpretations were as follows. First, symbolism of plants could be summarized as 1) Li Yu thought that Paeonia suffruticosa was called 'the king of flowers', not only because of its beauty, but also because of its upright character. The only flower that could compete against Paeonia suffruticosa was Paeonia lactiflora Pall.. This flower was called 'the flower prime minister' by common people. But Li Yu thought that Paeonia lactiflora Pall. should also be included in the feudal princes. 2) Prunus persica and Camellia japonica were compared to 'beautiful cheeks', and Malus spectabilis (Ait.)Borkh, Jasminum sambac, Rosa multiflora var. platyphylla, Narcissus tazetta, Papaver rhoeas were compared to 'beautiful women', expressing his love for flowers. 3) Li Yu called Nelumbo nucifera a 'gentleman in flowers' and Buxus sinica Rehd. et Wils. Cheng a 'gentleman in trees'. On the contrary, Daphne odora was compared to 'villain in flowers'. 4) Ilex integra was compared to a hermit, and Campsis grandiflora was compared to an immortal. Second, appreciation of plants could be organized by 1) Appreciation of plants required assistive tools. When going to suburb to enjoy the scenery, tents needed to be prepared. Paper screens should be used to appreciate Prunus mume in the courtyard so as to gain more elegant. Li Yu also proposed that ornamental objects should be properly placed near Orchidaceae so as to gain more elegant. 2）Li Yu took Lagerstroemia indica and Prunus armeniaca L. as examples to interpret that plants were as perceptive as animals and human beings. 3) Li Yu took Salix pierotii and Albizia julibrissin as examples to interpret that people should communicate with plants through five senses to produce resonance. And took Nelumbo nucifera and Rosa rugosa to emphasize the ornamental and practical value of plants. 4) Plants were metaphored sth. similar to them. An interesting example was Celosia cristata L. which was more like an auspicious cloud in the sky than the crest on the cock's head. To sum up, Li Yu personified plants and thought that people should communicate with plants by multi-sensory world when appreciating plants. Through this, it fully showed his love for plants. Meanwhile, Li Yu's symbolism and appreciation of plants, to some extent, reflected the elegant life of literati in the early Qing Dynasty.

• Journal of Korean Foot and Ankle Society
• /
• v.23 no.1
• /
• pp.24-30
• /
• 2019

Purpose: To evaluate the efficiency of the electronic foot function index (eFFI) through a prospective, random based, multi-institutional study. Materials and Methods: The study included 227 patients ranging in age from 20 to 79 years, visited for surgery in different 15 institutes, and agreed to volunteer. The patients were assigned randomly into a paper-based evaluated group (n=113) and tablet-based evaluated group (n=114). The evaluation was done on the day of hospital admission and the method was changed on the second day of surgery and re-evaluated. PADAS 2.0 (https://www.proscore.kr) was used as an electronic evaluation program. Results: There were no differences in age and sex in both groups. The intraclass correlation coefficient (ICC) evaluation revealed an eFFI ICC of 0.924, showing that both results were similar. The evaluation time was shorter in the tablet-based group than the paper-based group (paper vs tablet, $3.7{\pm}3.8$ vs $2.3{\pm}1.3minutes$). Thirty-nine patients (17.2%) preferred to use paper and 131 patients (57.7%) preferred the tablet. Fifty-seven patients (25.1%) found both ways to be acceptable. Conclusion: eFFI through tablet devices appears to be more constant than the paper-based program. In addition, it required a shorter amount of time and the patients tended to prefer the tablet-based program. Overall, tablet and cloud system can be beneficial to a clinical study.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.449-461
• /
• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.33-54
• /
• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.