Kim, Jee Sang;Kong, Chang In;Park, Bo Ryoung;Kim, Jeong-Hoon
Membrane Journal
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v.24
no.3
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pp.213-222
/
2014
In this study, 2-stage recirculation membrane process was developed for purification of high purity bio-methane for the vehicle fuel application. Pure gas permeation and mixture gas permeation test were done as a function of methane content and pressure in the feed using polysulfone membrane modules. 2-stage membrane plant was designed, constructed in a food waste treatment cite. Dehumidification, dry desulfurization, and desiloxane plants are installed for the removal of $H_2O$, $H_2S$ and siloxane in the biogas. Permeation test were done with the pre-treated methane mixture in terms of methane purity and recovery by adjusting the ratio of membrane area (1:1, 1:3, 2:2) in the first and second membrane modules in the plant. When membrane area of 2 stage increased to $3m^2$ from $1m^2$ at 1-stage membrane area of $1m^2$, the feed rate and $CH_4$ recovery at 95% methane purity were increased from 47.1% to 92.5% respectively. When the membrane area increased two-fold (1:1 to 2:2), $CH_4$ recovery increased from 47.1% to 88.3%. When the feed flow rate was increased, in 1:3 ratio, final purity of the methane is reduced, the methane recovery is increased. When operating pressure was increased, the feed rate was increased and recovery was slightly decreased. From this result, membrane area, feed pressure and feed rate could be the important factor to the performance of the membrane process.
Kim, Bom-Sahn;Jang, Sung-June;Eo, Jae-Seon;Park, Eun-Kyung;Kim, Yu-Kyeong;Kim, Jong-Min;Lee, Won-Woo;Kim, Sang-Eun
Nuclear Medicine and Molecular Imaging
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v.41
no.4
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pp.272-279
/
2007
Purpose: The aim of this study was to evaluate the discriminating nature of $^{123}I-FP-CIT$ SPECT in patients with parkinsonism. Methods: $^{123}I-FP-CIT$ SPECT images acquired from the 18 normal controls; NC ($60.4{\pm}10.0$ yr) and 237 patients with parkinsonism ($65.9{\pm}9.2$ yr) were analyzed. From spatialIy normalized images, regional counts of the caudate, putamen, and occipital lobe were obtained using region of interest method. Binding potential (BP) was calculated with the ratio of specific to nonspecific binding activity at equilibrium. Additionally, the BP ratio of putamen to caudate (PCR) and asymmetric Index (ASI) were measured. Results: BPs of NC $3.37{\pm}0.57,\; 3.10{\pm}0.41,\; 3.23{\pm}0.48$ for caudate, putamen, whole striatum, respectively) had no significant difference with those of essential tremor; ET ($3.31{\pm}0.64,\; 3.06{\pm}0.61,\; 3.14{\pm}0.63$) and Alzheimer's disease; AD (3.33 $\pm$0.60, 3.29$\pm$0.79, 3.31$\pm$0.70), but were higher than those of Parkinson's disease; PD (1.92$\pm$0.74, 1.39$\pm$0.68, 1.64$\pm$0.68), multiple system atrophy; MSA (2.36$\pm$1.07, 2.16$\pm$0.91, 2.26$\pm$0.96), and dementia with Lewy body; DLB (1.95$\pm$0.72, 1.64$\pm$0.65, 1.79$\pm$0.66)(p<0.005). PD had statisticalIy lower values of PER and higher values of ASI than those of NC (p<0.005). And PD had significantIy lower value of PCR, higher ASI and lower BP in the putamen and whole striatum than MSA (p<0.05). Conclusion: Dopamine transporter image of $^{123}I-FP-CIT$ SPECT was a good value in differential diagnosis of parkinsonism.
An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.
Kwon, Woo-Hyun;Kim, Woo-Suk;Kang, Thomas H.K.;Hong, Sung-Gul;Kwak, Yoon-Keun
Journal of the Korea Concrete Institute
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v.23
no.6
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pp.711-722
/
2011
Beam-column gravity or Intermediate Moment frames subjected to unexpected large displacements are vulnerable when no seismic details are provided, which is typical. Conversely, economic efficiency of those frames is decreased if unnecessary special detailing is applied as the beam and column size becomes quite large and steel congestion is caused by joint transverse reinforcement in beam-column connections. Moderate seismic design is used in Korea for beam-column connections of buildings with structural walls, which are to be destroyed when the unexpected large earthquake occurs. Nonetheless, performance of such beamcolumn connections may be substantially improved by the addition of steel fibers. This study was conducted to investigate the effect of steel fibers in reinforced concrete exterior beam-column connections and possibility for the replacement of some joint transverse reinforcement. Ten half-scale beam-column connections with non-seismic details were tested under cyclic loads with two cycles at each drift up to 19 cycles. Main test parameters used were the volume ratio of steel fibers (0%, 1%, 1.5%) and joint transverse reinforcement amount. The test results show that maximum capacity, energy dissipation capacity, shear strength and bond condition are improved with the application of steel fibers to substitute transverse reinforcement of beam-column connections. Furthermore, several shear strength equations for exterior connections were examined, including the proposed equation for steel fiber-reinforced concrete exterior connections with non-seismic details.
Korean Journal of Agricultural and Forest Meteorology
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v.18
no.4
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pp.307-319
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2016
A Land-Atmosphere Modeling Package (LAMP) for supporting agricultural and forest management was developed at the National Center for AgroMeteorology (NCAM). The package is comprised of two components; one is the Weather Research and Forecasting modeling system (WRF) coupled with Noah-Multiparameterization options (Noah-MP) Land Surface Model (LSM) and the other is an offline one-dimensional LSM. The objective of this paper is to briefly describe the two components of the NCAM-LAMP and to evaluate their initial performance. The coupled WRF/Noah-MP system is configured with a parent domain over East Asia and three nested domains with a finest horizontal grid size of 810 m. The innermost domain covers two Gwangneung deciduous and coniferous KoFlux sites (GDK and GCK). The model is integrated for about 8 days with the initial and boundary conditions taken from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data. The verification variables are 2-m air temperature, 10-m wind, 2-m humidity, and surface precipitation for the WRF/Noah-MP coupled system. Skill scores are calculated for each domain and two dynamic vegetation options using the difference between the observed data from the Korea Meteorological Administration (KMA) and the simulated data from the WRF/Noah-MP coupled system. The accuracy of precipitation simulation is examined using a contingency table that is made up of the Probability of Detection (POD) and the Equitable Threat Score (ETS). The standalone LSM simulation is conducted for one year with the original settings and is compared with the KoFlux site observation for net radiation, sensible heat flux, latent heat flux, and soil moisture variables. According to results, the innermost domain (810 m resolution) among all domains showed the minimum root mean square error for 2-m air temperature, 10-m wind, and 2-m humidity. Turning on the dynamic vegetation had a tendency of reducing 10-m wind simulation errors in all domains. The first nested domain (7,290 m resolution) showed the highest precipitation score, but showed little advantage compared with using the dynamic vegetation. On the other hand, the offline one-dimensional Noah-MP LSM simulation captured the site observed pattern and magnitude of radiative fluxes and soil moisture, and it left room for further improvement through supplementing the model input of leaf area index and finding a proper combination of model physics.
The construction procedures and artificial turf maintenance program on golf course definitely influence on the distortion of its environment. Soil microbial communities in soil profile were affected directly by those practises on turf areas. In Jeju island, the environmental impact assessment has been required to apply the first quality class granular activated carbon(GAC), which has a high absorbent character to agricultural chemicals, on the soil profiles of golf green system to reduce the pesticide leaching to ground water. This research was carried out to analyze the changes of microbial communities and chemical properties on soil profiles where GAC had been applied at the construction stage at two golf courses in Jeju. The changes of soil microbial population and chemical properties associated with construction methods of soil profile and agrochemical management program were analyzed by monthly at the surface and sub-soil profiles during April through October, 2007. The total numbers of bacteria and fungi, soil moisture content, soil physio-chemical properties were measured on greens and fairways of the both golf courses with different GAC treatment on the green and fairway soil profiles. The results showed that GAC had positive effects on the water holding capacity, pH and EC, however, it did not improved the holding capacity of available nutrients ${NO_3}^-,{NH_4}^+$, and phosphorus by its sorption phenomenon. In microbial count test, the total numbers of bacteria and fungi showed a great variation during sampling dates. That may directly relate to the agrochemical application, however, the ratio of total bacterial number versus total fungus number showed a constant value on a sub-soil of 15~30cm depth. Thus, the construction method of GAC in soil profile, and application of fertilizer and pesticide, both impacted on the changes of microbial population. It's means that the construction method of soil profile and turf management using agro-materials might greatly affect on the turfgrass culture and the environment of golf course.
In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.
Korean Journal of Agricultural and Forest Meteorology
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v.22
no.4
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pp.239-249
/
2020
Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.
The aim of this study is to establish a new QC method that can simultaneously evaluate the resolution of the x/y plane and the z-axis by producing a phantom that can reflect exposure and reconstruction parameter of MDCT system. It was used with Aquilion ONE(Cannon Medical System, Otawara, Japan), and the examination was scanned using of 120 kV, 260 mA, and the D-FOV of 300 mm2. It produced new SSP phantom modules in which two aluminum plates inclined at 45° to a vertical axis and a transverse axis to evaluate high contrast resolution of x/y plane and z axis. And it changed factors such as the algorithm, distance from gantry iso-center. All images were reconstructed in five steps from 0.6 mm to 10.0 mm slice thickness to measure resolution of x/y plane and z-axis. The image data measured FWHM and FWTM using Profile tool of Aquarius iNtusion Edition ver. 4.4.13 P6 software(Terarecon, California, USA), and analysed SPQI and signal intensity by ImageJ program(v1.53n, National Institutes of Health, USA). It decreased by 4.09~11.99%, 4.12~35.52%, and 4.70~37.64% in slice thickness of 2.5 mm, 5.0 mm, and 10.0 mm for evaluating the high contrast resolution of x/y plane according to distance from gantry iso-center. Therefore, the high contrast resolution of the x/y plane decreased when the distance from the iso-center increased or the slice thickness increased. Additionally, the slice thicknesses of 2.5 mm, 5.0 mm, and 10.0 mm with a high algorithm increased 74.83, 15.18 and 81.25%. The FWHM was almost constant on the measured SSP graph for evaluating the accuracy of slice thickness which represents the resolution of x/y plane and z-axis, but it was measured to be higher than the nominal slice thickness set by user. The FWHM and FWTM of z-axis with axial scan mode tended to increase significantly as the distance increased from gantry iso-center than the helical mode. Particularly, the thinner slice thickness that increased error range compare with the nominal slice thickness. The SPQI increased with thick slice thickness, and that was closer to 90% in the helical scan than the axial scan. In conclusion, by producing a phantom suitable for MDCT detectors and capable of quantitative resolution evaluation, it can be used as a specific method in the management of research quality and management of outdated equipment. Thus, it is expected to contribute greatly to the discrimination of lesions in the field of CT imaging.
Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.
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