• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.3
• /
• pp.41-47
• /
• 2015

To determine the accurate height, it should be considered geometric height difference obtained by levelling as well as the physical height difference what so called orthometric correction. The orthometric correction amount is small enough to ignore at flatland but the amount is big at high mountains, so it should be considered to obtain accurate height at high mountains. But the calculation process is difficult and complex, not easy to calculate. So, to make the process easy using a user-friendly visual, a orthometric correction calculation program Ortho-Calc. v1.0 was developed in this study. This program was adopted the algorithm of Nassar and Hwang & Hsiao, and Strang Van Hees and it could be to selectivily calculate the correction amount. The inspection result exhibited high accuracy with the standard deviation of 0.024mm by the comparison of previous study. Therefore, This program Ortho-Calc. v1.0 developed in this study, will contribute orthometric correction calculation quickly and easily. And, if this program is widely popular, it could be expected to make a contribution the Benchmark's official height renewal using orthometric correction.

• The Korean Journal of Ecology
• /
• v.26 no.3
• /
• pp.129-134
• /
• 2003

Amounts of CO₂ fixed by net primary production and released by soil respiration were determined on big-cone pine plantation. Net primary production, which was determined by allometric method, was converted into CO₂. CO₂ evolution in forest ecosystems are mainly through soil and root respiration. In order to separate root respiration from soil respiration, root-free sites were made in stand. Litter removal sites were prepared to estimate CO₂ evolution through litter layer. Respiration was measured at every two weeks intervals from April 2001 through April 2002, and soil temperature and soil moisture were measured at the same time. Net primary production of this big-cone pine plantation was 25.7 t·ha/sup -1/·yr/sup -1/. The amount of CO₂ fixed by this plantation was 42.5 t CO₂·ha/sup -1/·yr/sup -1/, The amount of CO₂ released by soil respiration was 5.0 t CO₂·ha/sup -1/·yr/sup -1/. The relative contribution of root respiration and litter layer respiration to total respiration was 46% and 32%, respectively. Net amount of fixed CO₂ was 37.5 t CO₂·ha/sup -1/·yr/sup -1/ in this big-cone pine plantation. From this result, this big-cone pine plantation play a carbon sink source from the atmosphere.

• Korean Journal of Soil Science and Fertilizer
• /
• v.45 no.6
• /
• pp.1143-1152
• /
• 2012

We established a top-down methodology to estimate carbon footprint as national mean value (reference) with the statistical data on agri-livestock incomes in 2007. We also established LCI (life cycle inventory) DB by a bottom-up methodology with the data obtained from interview with farmers from 4 large-scale farms at Gunsan, Jeollabuk-do province to estimate carbon footprint in 2011. This study was carried out to compare top-down methodology and bottom-up methodology in performing LCA (life cycle assessment) to analyze the difference in GHGs (greenhouse gases) emission and carbon footprint under conventional rice cultivation system. Results of LCI analysis showed that most of $CO_2$ was emitted during fertilizer production and rice cultivation, whereas $CH_4$ and $N_2O$ were mostly emitted during rice cultivation. The carbon footprints on conventional rice production system were 2.39E+00 kg $CO_2$-eq. $kg^{-1}$ by top-down methodology, whereas 1.04E+00 kg $CO_2$-eq. $kg^{-1}$ by bottom-up methodology. The amount of agro-materials input during the entire rice cultivation for the two methodologies was similar. The amount of agro-materials input for the bottom-up methodology was sometimes greater than that for top-down methodology. While carbon footprint by the bottom-up methodology was smaller than that by the top-down methodology due to higher yield per cropping season by the bottom-up methodology. Under the conventional rice production system, fertilizer production showed the highest contribution to the environmental impacts on most categories except GWP (global warming potential) category. Rice cultivation was the highest contribution to the environmental impacts on GWP category under the conventional rice production system. The main factors of carbon footprints under the conventional rice production system were $CH_4$ emission from rice paddy field, the amount of fertilizer input and rice yield. Results of this study will be used for establishing baseline data for estimating carbon footprint from 'low carbon certification pilot project' as well as for developing farming methods of reducing $CO_2$ emission from rice paddy fields.

• Korean Journal of Ecology and Environment
• /
• v.54 no.1
• /
• pp.49-60
• /
• 2021

We examined the flow characteristics by direct runoff and base flow in a headwater stream during observed 59 rainfall events of flood season (June~September) from 2017 to 2020 yrs. Total precipitation ranged from 5.0 to 400.8 mm, total runoff ranged from 0.1 to 176.5 mm, and runoff ratio ranged from 0.1 to 242.9% during the rainfall events. From hydrograph separation, flow duration in base flow (139.3 days) was tended to be longer than direct runoff (78.3 days), while the contribution of direct runoff in total runoff (54.2%) was greater than base flow (45.8%). The total amount and peak flow of direct runoff and base flow had the highest correlation (p<0.05) with total precipitation and duration of rain among rainfall and soil moisture conditions. Dominant rainfall events for the total amount and peak flow of base flow were generated under 5.0~200.4 and 10.5~110.5 mm in total precipitation. However, when direct runoff occurred as dominant rainfall events, total amount and peak flow were increased by 267.4~400.8 and 169.0~400.8 mm in total precipitation. Therefore, the unique aspects of our study design permitted us to draw inferences about flow characteristic analysis with the contribution of base flow and/or direct runoff in the total runoff in a headwater stream. Furthermore, it will be useful for the long-term strategy of effective water management for integrated surface-groundwater in the forested headwater stream.

• Asian Journal of Atmospheric Environment
• /
• v.5 no.4
• /
• pp.237-246
• /
• 2011

To explore the sources of carbonaceous material in the airborne particulate matter (PM), comprehensive PM sampling was performed (3 to 14 January 2010) at a traffic hot spot site (HS), Farm Gate, Dhaka using several samplers: AirMetrics MiniVol (for $PM_{10}$ and $PM_{2.5}$) and MOUDI (for size fractionated submicron PM). Long-term PM data (April 2000 to March 2006 and April 2000 to March 2010 in two size fractions ($PM_{2.2}$ and $PM_{2.2-10}$) obtained from two air quality-monitoring stations, one at Farm Gate (HS) and another at a semi-residential (SR) area (Atomic Energy Centre, Dhaka Campus, (AECD)), respectively were also analyzed. The long-term PM trend shows that fine particulate matter concentrations have decreased over time as a result of government policy interventions even with increasing vehicles on the road. The ratio of $PM_{2.5}/PM_{10}$ showed that the average $PM_{2.5}$ mass was about 78% of the $PM_{10}$ mass. It was also found that about 63% of $PM_{2.5}$ mass is $PM_1$. The total contribution of BC to $PM_{2.5}$ is about 16% and showed a decreasing trend over the years. It was observed that $PM_1$ fractions contained the major amount of carbonaceous materials, which mainly originated from high temperature combustion process in the $PM_{2.5}$. From the IMPROVE TOR protocol carbon fraction analysis, it was observed that emissions from gasoline vehicles contributed to $PM_1$ given the high abundance of EC1 and OC2 and the contribution of diesel to $PM_1$ is minimal as indicated by the low abundance of OC1 and EC2. Source apportionment results also show that vehicular exhaust is the largest contributors to PM in Dhaka. There is also transported $PM_{2.2}$from regional sources. With the increasing economic activities and recent GDP growth, the number of vehicles and brick kilns has significantly increased in and around Dhaka. Further action will be required to further reduce PM-related air pollution in Dhaka.

• Journal of Korean Society for Atmospheric Environment
• /
• v.30 no.6
• /
• pp.555-568
• /
• 2014

The construction equipment is one of the major sources for hazardous air pollutants in Korea, and the its management has been of great concern recently. The objective of this study was to estimate each contribution of emission of construction equipments according to their production year, electric power consumption and type. To achieve this goal, we developed pollutant emission factors for the machineries manufactured after 2009, which are excluded from the present framework of Korean air pollutants inventory, CAPSS. More than 800 data obtained from emission investigations were utilized for the estimation. Compared with the previous estimation, the scheme used this study was modified to incorporate new emission factors as well as to include the corresponding activity data. Such improvement allow us to gain more detailed emission informations which are better characterized by specifications of construction equipments. The total amount of pollutants emitted from construction equipments in 2011 were estimated as 126.8, 7.0, 58.3, and 17.0 kton for $NO_x$, PM, CO, and VOC, respectively. The estimation results indicate that the increase in the emission of equipments is significantly related to their age and power consumption. The emissions of the older ones manufactured from 1992~1996 were estimated to be the contribution ranged from 23.7% to 26.8%, whereas the newer ones (2009~2011) showed the attributions of 11.3~21.5%. In addition, the results show that the emission of each equipment was increased with the increase in the electric power consumption of engine, probably due to their average output power. Among the nine types of machinery compared, excavators and forklifts were investigated to contribute relatively higher emissions in the level of 39.8~44.0% and 32.0~34.2%, respectively.

• The Journal of the Korea Contents Association
• /
• v.16 no.3
• /
• pp.676-693
• /
• 2016

Although the methodology and models to assess the economic value of technology assets such as patents are being presented in various ways, there does not exist a structured assessment model which enables to objectively assess a database property's value, and thus there is a need to enhance the application feasibility of practical purposes such as licensing of DB assets, commercialization transfer, security, etc., through the establishment of the valuation model and the life-cycle decision logic. In this study, during the valuation process of DB assets, the size of customer demand group expected and the amount of demand, the size and importance of data sets, the approximate degree of database' contribution to the sales performance of a company, the life-cycle of database assets, etc. will be analyzed whether they are appropriate as input variables or not. As for most of DB assets, due to irregular updates there are hardly cases their life-cycle expires, and thus software package's persisting period, ie. 5 years, is often considered the standard. We herein propose the life-cycle estimation logic and valuation models of DB assets based on the concept of half life for DB usage frequency under the condition that DB assets' value decays and there occurs no data update over time.

• Korean Chemical Engineering Research
• /
• v.48 no.6
• /
• pp.717-724
• /
• 2010

The flash point is one of the most important physical properties used to determine the potential for fire and explosion hazards of flammable liquids. Despite the needs of the experimental flash point data for the design and construction of chemical plants, there is often a significant gap between the demands for the data and their availability. This study have built and compared two models of partial least squares(PLS) and support vector machine(SVM) to predict the experimental flash points of 893 organic compounds out of DIPPR 801. As the independent variables of the models, 65 functional groups were chosen based on the group contribution method that was oriented from the assumption that each fragment of a molecule contributes a certain amount to the value of its physical property, and the logarithm of molecular weight was added. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, an optimization technique should be used to get three parameters of SVM model. This work adopted particle swarm optimization that is one of heuristic optimization methods. As the selection of training data can affect the prediction performance, 100 data sets of randomly selected data were generated and tested. The PLS and SVM results of the average absolute errors for the whole data range from 13.86 K to 14.55 K and 7.44 K to 10.26 K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.3
• /
• pp.261-270
• /
• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of Environmental Health Sciences
• /
• v.45 no.6
• /
• pp.630-637
• /
• 2019

Objectives: This study was performed to evaluate the secular changes in indoor airborne dust or endotoxin levels in the dust from swine confinement buildings. Indoor levels were compared with the level at the exhaust outlet in order to examine the contribution potential of indoor dust to nearby ambient air dust. Methods: Comparisons were made on inhalable and respirable dust levels reported in 2002, 2012, and 2017 from 14, 10, and 36 swine fattening confinement buildings in Korea, respectively. This data was produced by the same research group. Levels of endotoxin adsorbed into inhalable or respirable dust were also compared. Samples of inhalable or respirable dust were collected indoors and at exhaust outlets from 17 swine fattening confinement buildings in 2019, and dust levels were compared between the indoor and the outlet. Results: The outlet inhalable dust level (0.111 mg/㎥) was approximately 19% of that from indoors, and the respirable dust level (0.033 mg/㎥) was approximately 74% of that from indoors. The outlet respirable dust levels were lower than the airborne fine dust levels in the towns where those farms are located. No significant difference was observed in the inhalable dust levels among the years examined, but the respirable dust level in 2017 (0.143 mg/㎥) was significantly lower than in 2002 (0.328 mg/㎥). The level of endotoxin in inhalable dust was significantly higher in 2017 (722 EU/㎥) than in both 2002 (75 EU/㎥) and 2012 (171 EU/㎥). Conclusion: Even though no apparent contribution from swine farm indoor dust to nearby ambient air dust was observed in terms of amount, a certain control strategy to reduce the production of airborne dust and endotoxin from swine farms is merited.