• The Journal of Korean Society for Radiation Therapy
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• v.25 no.2
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• pp.159-165
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• 2013

Purpose: Abdominal compressor is used to control breathing in stereotactic body radiotherapy for lung tumors frequently. We evaluated the dynamic variation aspect of internal tumor volume by breathing. Materials and Methods: We reviewed 20 lung cancer patients (7 upper lung patients, 4 middle lung patients, 9 lower lung patients) who received stereotactic body radiotherapy using abdominal compressor between April 2012 to April 2013. Coordinate shift values were obtained by using four-dimensional cone-beam CT (4D-CBCT) to investigate treatment set-up error and moving tumor position error. To investigate how much difference of each part, we compared 95% confidence interval, maximum values and minimum values of three-dimensional vector value and analyzed conformity degree through the Pearson square correlation coefficient. Results: 95% confidence interval of three-dimensional vector value of each part is 1.8~2.9 mm in upper lobe, 2.3~5.4 mm in middle lobe and 2.2~4.0 mm in lower lobe. Conformity degree was the result that respectively is LR direction 0.75, SI direction 0.68 and AP direction 0.63 in upper lobe, LR direction 0.82, SI direction 0.51 and AP direction 0.92 in middle lobe and LR direction 0.63, SI direction 0.50 and AP direction 0.34 in lower lobe. Conclusion: We showed difference by each site in lung tumor due to respiration by using abdominal compressor. Therefore, we must correct treatment set-up error as well as moving tumor position error by breathing. It is also considered to be useful that it is the use of 4D-CBCT when correcting the error due to various dynamic variation.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.515-522
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• 2011

The hydrodynamic models are widely used in the research for analysis of flow characteristics and design of hydraulic structure and river channel. These models need to be calibrated with observed data. But, there are few field data of two-dimensional flow velocity in flood because the direct measurement of the flood flow velocity are very dangerous. For this reason the results of two-dimensional numerical models are usually calibrated and verified with only a few observed data. Moreover, the verification of numerical models for the design flood is usually carried out using the result of one-dimensional model, HEC-RAS. In this study, using the flow velocity profile extracted from the aerial photos of a flood of the Tone River in Japan, two-dimensional numerical models, RAM2 in RAMS, RMA2 in SMS, and one-dimensional numerical model, HEC-RAS which are most widely used in research and design work are verified and the validity for verification of two-dimensional models with HEC-RAS is reviewed. The results showed that the water surface elevation of HEC-RAS, RAM2 and RMA2 models have similar results with observed data. But, the velocity results of RAM2 and RMA2 models in the floodplain have some difference with the velocity from aerial photo analysis. And the velocity result of HEC-RAS has big difference with the sectional averaged value of velocity from aerial photo analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.6
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• pp.623-629
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• 2002

In transmitting compressed video bit-stream over Internet, packet losses cause error propagations in both spatial and temporal domains, which in turn leads to severe degradation I image quality. In this paper, a new error concealment algorithm, called EBMA(Edge Detection based Boundary Matching Algorithm), is proposed to repair damaged portions of the video frames in the receiver. Conventional BMA(Boundary Matching Algorithm) assumes that the pixels on the boundary of the missing block and its neighboring blocks are very similar, but has no consideration of edges across the boundary. In our approach, the edges are detected across the boundary of the lost or erroneous block. Once the orientation of each edge is found, only the pixel difference along the expected edges across the boundary is measured instead of the calculation of difference along the expected edges across the boundary is measured instead of the calculation of differences between all adjacent pixels on the boundary Therefore, the proposed approach needs very few computations and the experiment shows and improvement of the performance over the conventional BMA in terms of both subjective and objective quality of video sequences.

• Korean journal of applied entomology
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• v.49 no.3
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• pp.211-218
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• 2010

Cuticular hydrocarbons (CHCs) of the pine sawyer (Monochamus saltuarius), Japanese pine sawyer (M. alternatus) and oak longicorn beetle (Moechotypa diphysis) were analyzed by GC, GC-MS and compared. Monochamus beetles are typical vectors of pine wilt disease but Moechotypa diphysis, which belongs to the same family, is not. They possess different CHCs in carbon number: 23-25 in M. saltuarius, 25-32 in M. alternatus, and 23-29 in M. diphysis. In comparison to inter-species, these three species of adult beetles have different numbers and chains of constituents of CHCs. In comparison between male and female in intra-species, the quantities of CHCs show the difference but constituents are not. Major constituent of M. saltuarius were analyzed as n-pentacosane > n-nonacosane > n-heptacosane; those of M. alternatus were n-nonacosene > n-pentacosane > n-nonacosane; and those of M. diphysis were n-heptacosane > 13-methylheptacosane > 3-methylheptacosane. From the body surface, most saturated carbohydrates of 3 species beetles are composed of n-alkane (40.2 - 65.7%) and followed by olefines > monomethylalkanes that one or two double bonds in M. saltuarius and M. alternatus. Otherwise, M. diphysis have the difference in order of monomethylalkanes > olefins.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.40-51
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• 2014

This study aims to investigate urban inundation considering building footprints based on dual-drainage scheme. For this purpose, LiDAR data is cultivated to generate two original data set in terms of DEM with $1{\times}1$ meter and building layer of the study drainage area in Seoul and then the building layer is overlapped as vector polygon with the mesh data with the same size as DEM. Then, terrain data for modeling were re-sampled to reduce resolution as $10{\times}10$ meters. As results, the simulated depth without considering building footprints has a tendency to underestimate the inundation depth compared to observed data analized by CCTV imagery. Otherwise, the simulation result considering building footprints revealed definitely higher fitness. The difference of inundation depth came from the variation of inundation volume which was relevant to inundation extent. If the building footprints are enlarged, the possible inundation depth is increased, which results in being inundation depth higher because hydrological conditions such as rainfall depth are conservational. Otherwise, according to comparison of inundation extents, there were no significant difference but the case of considering building footprint was revealed slightly higher fitness. Thus, it is concluded that the considering building footprint for inundation analysis of urban watershed should be required to improve simulation accuracy synthetically.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.19-29
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• 2011

Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.88-96
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• 2014

In modern electronic warfare systems, the necessity of a more accurate estimation method based on non-AOA (arrival of angle) measurement, such as TDOA and FDOA, have been increased. The previous researches using single TDOA have been carried out in terms of not only the development of emitter location algorithms but also the enhancement of measurement accuracy. Recently, however, the combined TDOA/FDOA method is of considerable interest because it is able to estimate the velocity vector of a moving emitter and acquire a pair of TDOA and FDOA measurements from a single sensor pair. In this circumstance, it is needed to derive the required FDOA measurement accuracy in order that the TDOA/FDOA combined localization system outperforms the previous single TDOA localization systems. Therefore, we analyze the contribution of FDOA measurement accuracy to emitter location, then propose the criterion based on CRLB (Cramer-Rao lower bound). Simulations are included to examine the validity of the proposed criterion by using the Gauss-Newton algorithm.