• Journal of Climate Change Research
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• v.6 no.3
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• pp.175-184
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• 2015

In this study GC and PAS were used to calculate $N_2O$ concentration of exhaust gas from Wood Chip combustion system. Fuel supplied to the incinerator was collected and analyzed and then the analysis result was used to calculate $N_2O$ emissions. Tier 3 and Tier 4 Method were used to calculate the $N_2O$ emissions. Plant's Specific emission factor of $N_2O$ by Tier 3 Method was 0.35 kg/TJ, while default emission factor of Wood?Wood Waste proposed by 2006 IPCC G/L was 4 kg/TJ. So the $N_2O$ emission factor of this study was 3.65 kg/TJ lower compared to the IPCC G/L. The total emissions calculated by Plant's specific emission factor was 4.22 kg during the measuring period, but by Tier 4 Method it was 7.88 kg. This difference in emissions was caused by the difference of continuous measuring and intermittent sampling. It would be necessary to apply continuous measuring to calculate emissions of $Non-CO_2$ gas whose the density distribution is relatively high. However currently, according to the target management guideline of greenhouse gas and energy, the continuous measuring method to calculate greenhouse gas emission is applied only to $CO_2$. Therefore for reliable greenhouse gas emission calculation it would be necessary to apply continuous measuring to calculate $Non-CO_2$ gas emission.

• Journal of agriculture & life science
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• v.52 no.6
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• pp.37-47
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• 2018

The objective of this study was to analyze the influences of forest management activity on the diameter distribution of Larix kaempferi Carriere stands in South Korea. We used 232 managed stands data, 47 unmanaged stands data of National Forest Inventory for this study. We employed the Weibull distribution function for estimating diameter based on percentiles and parameter recovery method. The results revealed that the average diameter breast height movements and growth of tree in the managed stands higher than the unmanaged stands according to the scenario: age, site index, and tree density change. The finding shows the percentage of the total amount of large class diameter was also high in the managed stands. The results of this study could be apply for the estimation of multi-products of timbers per diameter classes and stand structure development for Larix kaempferi Carriere stands in South Korea.

• Geomechanics and Engineering
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• v.17 no.1
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• pp.81-95
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• 2019

Due to the importance of soil reinforcement using geotextiles in geotechnical engineering, study and investigation into long-term performance, design life and survivability of geotextiles, especially due to installation damage are necessary and will affect their economy. During installation, spreading and compaction of backfill materials, geotextiles may encounter severe stresses which can be higher than they will experience in-service. This paper aims to investigate the installation damage of geotextiles, in order to obtain a good approach to the estimation of the material's strength reduction factor. A series of full-scale tests were conducted to simulate the installation process. The study includes four deliberately poorly-graded backfill materials, two kinds of subgrades with different CBR values, three nonwoven needle-punched geotextiles of classes 1, 2 and 3 (according to AASHTO M288-08) and two different relative densities for the backfill materials. Also, to determine how well or how poorly the geotextiles tolerated the imposed construction stresses, grab tensile tests and visual inspections were carried out on geotextile specimens (before and after installation). Visual inspections of the geotextiles revealed sedimentation of fine-grained particles in all specimens and local stretching of geotextiles by larger soil particles which exerted some damage. A regression model is proposed to reliably predict the installation damage reduction factor. The results, obtained by grab tensile tests and via the proposed models, indicated that the strength reduction factor due to installation damage was reduced as the median grain size and relative density of the backfill decreases, stress transferred to the geotextiles' level decreases and as the as-received grab tensile strength of geotextile and the subgrades' CBR value increase.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.266-272
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• 2011

This study was conducted to analyze the characteristics of crown fuel biomass and to develop allometric equations for the estimation of crown fuel biomass by subjectively categorized the crown component in Pinus densiflora stands. A total of ten representative trees were destructively sampled in Youngju region. Crown fuel were weighed separately for each fuel category by size class. The results of this study showed that foliar moisture content was 119% while the average crown moisture content was 105.3%. The crown fuel/total fuel loading ratio was 30%, needles and twigs with less than 1 cm diameter accounted 50.3% for its fuel/crown fuel loading ratio. Adjusted multiple coefficient of determination of suggested allometric equations ranged from 0.6846 to 0.9246 for crown fuel biomass, 0.8308 for crown volume.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.531-538
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• 2021

In this study, the TOC in six estuarine reservoirs in the West Sea (Ganwol, Namyang, Daeho, Bunam, Sapkyo, and Asan) was estimated using optically-active water quality factors by the water environment monitoring network. First, specification data and land use maps of each estuarine reservoir were collected. Subsequently, water quality data from 2013 to 2020 were collected. The data comprised of 11 parameters: pH, dissolved oxygen, BOD, COD, suspended solids (SS), total nitrogen, total phosphorus, water temperature, electrical conductivity, total coliforms, and chlorophyll-a (Chl-a). The TOC in the estuarine reservoirs was 4.9~7.0 mg/L, with the highest TOC of 7.0 mg/L observed at the Namyang reservoir, which has a low shape coefficient and high drainage density. The correlation of TOC with water quality factors was also analyzed, and the correlation coefficients of Chl-a and SS were 0.28 and 0.19, respectively, while the correlation coefficients of these factors in the Namyang reservoir were 0.42 and 0.27, respectively. To improve the estimation of TOC using Chl-a and SS, the TOC was averaged in 5 mg/L units, and Chl-a and SS were averaged. Correlation analysis was then performed and the R² of Chl-a-TOC was 0.73. The R² of SS-TOC was 0.73 with a non-linear relationship. TOC had a significant non-linear relationship with Chl-a and SS. However, the relationship should be assessed in terms of the spatial and temporal variations to construct a reliable remote sensing system.

• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.149-159
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• 2021

This paper represents a reentry time prediction analysis of CZ-5B rocket-body in China, subject to analysis of the Inter-Agency Space Debris Coordination Committee Reentry (IADC) reentry test campaign conducted in May 2021. Predicting the reentry of space objects is difficult to accurately predict due to the lack of accurate physical information about target, and uncertainty in atmospheric density. Therefore, IADC conducts annual re-entry campaigns to verify analysis techniques by each agency, and the Korea Aerospace Research Institute has also participated in them since 2015. Ballistic coefficient estimation method proposed to predict target reentry time and the result confirmed the difference of 73 seconds, which confirms the accuracy of the proposed method.

• Journal of Astronomy and Space Sciences
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• v.39 no.3
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• pp.117-126
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• 2022

The Ionospheric Anomaly Monitoring by Magnetometer And Plasma-probe (IAMMAP) is one of the scientific instruments for the Compact Advanced Satellite 500-3 (CAS 500-3) which is planned to be launched by Korean Space Launch Vehicle in 2024. The main scientific objective of IAMMAP is to understand the complicated correlation between the equatorial electro-jet (EEJ) and the equatorial ionization anomaly (EIA) which play important roles in the dynamics of the ionospheric plasma in the dayside equator region. IAMMAP consists of an impedance probe (IP) for precise plasma measurement and magnetometers for EEJ current estimation. The designated sun-synchronous orbit along the quasi-meridional plane makes the instrument suitable for studying the EIA and EEJ. The newly-devised IP is expected to obtain the electron density of the ionosphere with unprecedented precision by measuring the upper-hybrid frequency (f_UHR) of the ionospheric plasma, which is not affected by the satellite geometry, the spacecraft potential, or contamination unlike conventional Langmuir probes. A set of temperature-tolerant precision fluxgate magnetometers, called Adaptive In-phase MAGnetometer, is employed also for studying the complicated current system in the ionosphere and magnetosphere, which is particularly related with the EEJ caused by the potential difference along the zonal direction.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.