• Progress in Medical Physics
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• v.26 no.1
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• pp.28-35
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• 2015

In this study, we investigated the effects of an iterative reconstruction algorithm and an automatic exposure control (AEC) technique on image quality and radiation dose through phantom experiments with coronary computed tomography (CT) angiography protocols. We scanned the AAPM CT performance phantom using 320 multi-detector-row CT. At the tube voltages of 80, 100, and 120 kVp, the scanning was repeated with two settings of the AEC technique, i.e., with the target standard deviations (SD) values of 33 (the higher tube current) and 44 (the lower tube current). The scanned projection data were reconstructed also in two ways, with the filtered back projection (FBP) and with the iterative reconstruction technique (AIDR-3D). The image quality was evaluated quantitatively with the noise standard deviation, modulation transfer function, and the contrast to noise ratio (CNR). More specifically, we analyzed the influences of selection of a tube voltage and a reconstruction algorithm on tube current modulation and consequently on radiation dose. Reduction of image noise by the iterative reconstruction algorithm compared with the FBP was revealed eminently, especially with the lower tube current protocols, i.e., it was decreased by 46% and 38%, when the AEC was established with the lower dose (the target SD=44) and the higher dose (the target SD=33), respectively. As a side effect of iterative reconstruction, the spatial resolution was decreased by a degree that could not mar the remarkable gains in terms of noise reduction. Consequently, if coronary CT angiogprahy is scanned and reconstructed using both the automatic exposure control and iterative reconstruction techniques, it is anticipated that, in comparison with a conventional acquisition method, image noise can be reduced significantly with slight decrease in spatial resolution, implying clinical advantages of radiation dose reduction, still being faithful to the ALARA principle.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.481-494
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• 2011

Investigation of the $CO_2$ exchange between biosphere and atmosphere at regional, continental, and global scales can be directed to combining remote sensing with carbon cycle process to estimate vegetation productivity. NASA Earth Observing System (EOS) currently produces a regular global estimate of gross primary productivity (GPP) and annual net primary productivity (NPP) of the entire terrestrial earth surface at 1 km spatial resolution. While the MODIS GPP algorithm uses meteorological data provided by the NASA Data Assimilation Office (DAO), the sub-pixel heterogeneity or complex terrain are generally reflected due to coarse spatial resolutions of the DAO data (a resolution of $1{\circ}\;{\times}\;1.25{\circ}$). In this study, we estimated inputs retrieved from MODIS products of the AQUA and TERRA satellites with 5 km spatial resolution for the purpose of finer GPP and/or NPP determinations. The derivatives included temperature, VPD, and solar radiation. Seven AmeriFlux data located in the Corn Belt region were obtained to use for evaluation of the input data from MODIS. MODIS-derived air temperature values showed a good agreement with ground-based observations. The mean error (ME) and coefficient of correlation (R) ranged from $-0.9^{\circ}C$ to $+5.2^{\circ}C$ and from 0.83 to 0.98, respectively. VPD somewhat coarsely agreed with tower observations (ME = -183.8 Pa ~ +382.1 Pa; R = 0.51 ~ 0.92). While MODIS-derived shortwave radiation showed a good correlation with observations, it was slightly overestimated (ME = -0.4 MJ $day^{-1}$ ~ +7.9 MJ $day^{-1}$; R = 0.67 ~ 0.97). Our results indicate that the use of inputs derived MODIS atmosphere and land products can provide a useful tool for estimating crop GPP.

• Journal of the Korea Computer Graphics Society
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• v.26 no.3
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• pp.123-131
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• 2020

This study proposes a real-time rendering algorithm for lighting when each of more than 100,000 moving particles exists as a light source. Two 3D textures are used to dynamically determine the range of influence of each light, and the first 3D texture has light color and the second 3D texture has light direction information. Each frame goes through two steps. The first step is to update the particle information required for 3D texture initialization and rendering based on the Compute shader. Convert the particle position to the sampling coordinates of the 3D texture, and based on this coordinate, update the colour sum of the particle lights affecting the corresponding voxels for the first 3D texture and the sum of the directional vectors from the corresponding voxels to the particle lights for the second 3D texture. The second stage operates on a general rendering pipeline. Based on the polygon world position to be rendered first, the exact sampling coordinates of the 3D texture updated in the first step are calculated. Since the sample coordinates correspond 1:1 to the size of the 3D texture and the size of the game world, use the world coordinates of the pixel as the sampling coordinates. Lighting process is carried out based on the color of the sampled pixel and the direction vector of the light. The 3D texture corresponds 1:1 to the actual game world and assumes a minimum unit of 1m, but in areas smaller than 1m, problems such as stairs caused by resolution restrictions occur. Interpolation and super sampling are performed during texture sampling to improve these problems. Measurements of the time taken to render a frame showed that 146 ms was spent on the forward lighting pipeline, 46 ms on the defered lighting pipeline when the number of particles was 262144, and 214 ms on the forward lighting pipeline and 104 ms on the deferred lighting pipeline when the number of particle lights was 1,024766.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.366-377
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• 2016

Water use efficiency (WUE) is considered as an important ecological indicator which may provide information on the process-structure relationships associated with energy-matter-information flows in ecosystem. The WUE at ecosystem-level can be defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). In this study, KoFlux's long-term (2007-2015) eddy covariance measurements of $CO_2$ and water vapor fluxes were used to examine the WUE of needle fir plantation in Korea National Arboretum. Our objective is to ascertain the seasonality and inter-annual variability in WUE of this needle fir plantation so that the results may be assimilated into the development of a holistic ecological indicator for resilience assessment. Our results show that the WUE of needle fir plantation is characterized by a concave seasonal pattern with a minimum ($1.8-3.3g\;C{\cdot}(kg\;H_2O)^{-1}$) in August and a maximum ($5.1-11.4g\;C{\cdot}(kg\;H_2O)^{-1}$) in February. During the growing season (April to October), WUE was on average $3.5{\pm}0.3g\;C\;(kg\;H_2O)^{-1}$. During the dormant seasons (November to March), WUE showed more variations with a mean of $7.4{\pm}1.0g\;C{\cdot}(kg\;H_2O)^{-1}$. These values are in the upper ranges of WUE reported in the literature for coniferous forests in temperate zone. Although the growing season was defined as the period from April to October, the actual length of the growing season (GSL) varied each year and its variation explained 62% of the inter-annual variability of the growing season WUE. This is the first study to quantify long-term changes in ecosystem-level WUE in Korea and the results can be used to test models, remote-sensing algorithms and resilience of forest ecosystem.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.15-22
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• 2002

Transportation project appraisal should be precise in order to increase the social welfare and efficiency, and it has been evaluated by only a single criterion analysis such as benefit/cost analysis. However, this method cannot assess some qualitative items, and cannot get a proper solution for the clash of interests among various groups. Therefore, the multi-criteria analysis, which can control these problems, is needed, and then Saaty has developed one of these methods, AHP(Analytic Hierarchy Process) method. In AHP, the project is evaluated through weighted score of the criteria and the alternatives, which is surveyed by a questionnaire of specialists. It is based on some strict suppositions such as reciprocal comparison, homogeneity, expectation, independence relationship between multi-criteria, but supposing that each criterion has independence relation with others is too difficult in two reasons. First, in real situation, there cannot be perfect independence relationship between standards. Second, individuals, even though they are specialists of that area, do not feel the degree of independence relation as same as others. This paper develops a modified AHP method for solving this dependence relationship between multi-criteria. First of all. in this method, the degree of dependence relationship between multi-criteria that the specialist feels is surveyed and included to the weighted score of multi-criteria This study supposes three methods to implement this idea. The first model products the degree of dependence relationship in the first step for calculating the weighted score, and the others adjust the result of weighted score from the basic AHP method to the dependence relationship. One of the second methods distributes the cross weighted score to each standard by constant ratio, and the other splits them using Fuzzy measure such as Bel and Pl. Finally, in order to validate these methods, this paper applies them to evaluate the alternatives which can control public resentments against Korean rail path in a city area.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1041-1053
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• 2018

In this study, a real-time storage level and capacity monitoring and forecasting system for Yongdam Dam watershed was developed using high resolution satellite image. The drought indices such as Standardized Precipitation Index (SPI) from satellite data were used for storage level monitoring in case of drought. Moreover, to predict storage volume we used a statistical method based on Principle Component Analysis (PCA) of Singular Spectrum Analysis (SSA). According to this study, correlation coefficient between storage level and SPI (3) was highly calculated with CC=0.78, and the monitoring and predictability of storage level was diagnosed using the drought index calculated from satellite data. As a result of analysis of principal component analysis by SSA, correlation between SPI (3) and each Reconstructed Components (RCs) data were highly correlated with CC=0.87 to 0.99. And also, the correlations of RC data with Normalized Water Surface Level (N-W.S.L.) were confirmed that has highly correlated with CC=0.83 to 0.97. In terms of high resolution satellite image we developed a water detection algorithm by applying an exponential method to monitor the change of storage level by using Multi-Spectral Instrument (MSI) sensor of Sentinel-2 satellite. The materials of satellite image for water surface area detection in Yongdam dam watershed was considered from 2016 to 2018, respectively. Based on this, we proposed the possibility of real-time drought monitoring system using high resolution water surface area detection by Sentinel-2 satellite image. The results of this study can be applied to estimate of the reservoir volume calculated from various satellite observations, which can be used for monitoring and estimating hydrological droughts in an unmeasured area.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.147-157
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• 2022

This study tried to propose a smart farm technology strategy suitable for the domestic situation, focusing on the differentiation suitable for the domestic situation of ICT technology. In the case of advanced countries in the overseas agricultural industry, it was confirmed that they focused on the development of a specific stage that reflected the geographical characteristics of each country, the characteristics of the agricultural industry, and the characteristics of the people's demand. Confirmed that no enemy development is being performed. Therefore, in response to problems such as a rapid decrease in the domestic rural population, aging population, loss of agricultural price competitiveness, increase in fallow land, and decrease in use rate of arable land, this study aims to develop smart farm ICT technology in the future to create quality agricultural products and have price competitiveness. It was suggested that the smart farm should be promoted by paying attention to the excellent performance, ease of use due to the aging of the labor force, and economic feasibility suitable for a small business scale. First, in terms of economic feasibility, the ICT technology is configured by selecting only the functions necessary for the small farm household (primary) business environment, and the smooth communication system with these is applied to the ICT technology to gradually update the functions required by the actual farmhouse. suggested that it may contribute to the reduction. Second, in terms of performance, it is suggested that the operation accuracy can be increased if attention is paid to improving the communication function of ICT, such as adjusting the difficulty of big data suitable for the aging population in Korea, using a language suitable for them, and setting an algorithm that reflects their prediction tendencies. Third, the level of ease of use. Smart farms based on ICT technology for the development of the Industry6.0 (1.0(Agriculture, Forestry) + 2.0(Agricultural and Water & Water Processing) + 3.0 (Service, Rural Experience, SCM)) perform operations according to specific commands, finally suggested that ease of use can be promoted by presetting and standardizing devices based on big data configuration customized for each regional environment.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.575-583
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• 2021

This study aims to analyze Taekwondo trends according to news articles by year by applying topic modeling. In order to examine the Taekwondo trend through media reports, articles including news articles and Taekwondo specialized media articles were collected through Big Kinds of the Korea Press Foundation. The search period was divided into three sections: before 2000, 2001~2010, and 2011~2020. A total of 12,124 items were selected as research data. For topic analysis, pre-processing was performed, and topic analysis was performed using the LDA algorithm. In this case, python 3 was applied for all analysis. First, as a result of analyzing the topics of media articles by year, 'World' was the most common keyword before 2000. 'South and North Korea' was next common and 'Olympic' was the third commonest topic. From 2001 to 2010, 'World' was the most common topic, followed by 'Association' and 'World Taekwondo'. From 2011 to 2020, 'World', 'Demonstration', and 'Kukkiwon' was the most common topic in that order. Second, as a result of analyzing news articles before 2000 by topic modeling, topics were divided into two categories. Specifically, Topic 1 was selected as 'South-North Korea sports exchange' and Topic 2 was selected as 'Adoption of Olympic demonstration events'. Third, as a result of analyzing news articles from 2001 to 2010 by topic modeling, three topics were selected. Topic 1 was selected as 'Taekwondo Demonstration Performance and Corruption', Topic 2 was selected as 'Muju Taekwondo Park Creation', and Topic 3 was selected as 'World Taekwondo Festival'. Fourth, as a result of analyzing news articles from 2011 to 2020 by topic modeling, three topics were selected. Topic 1 was selected as 'Successful Hosting of the 2018 Pyeongchang Winter Olympics', Topic 2 was selected as 'North-South Korea Taekwondo Joint Demonstration Performance', and Topic 3 was selected as '2017 Muju World Taekwondo Championships'.