• Polymer(Korea)
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• v.38 no.1
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• pp.38-42
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• 2014

The poly[(9,9-bis((6'-(N,N,N-trimethylammonium)hexyl)-2,7-fluorene)-alt-(9,9-bis(2-(2-(2-methoxyethoxy)ethoxy)ethyl)-9-fluorene)) dibromide (WPF-6-oxy-F)] and graphene oxide (GO) was blended and irradiated with gamma ray under ambient condition. This WPF-6-oxy-F-GO composite was investigated as a hole-transporting layer (HTL) in organic solar cells (OSCs). Compared with the pristine GO, the sheet resistance ($R_{sheet}$) of irradiated WPF-6-oxy-F-GO was decreased about 2 orders of magnitude. The reason for the decrease of $R_{sheet}$ is the effect of efficient ${\pi}-{\pi}$ packing resulted from the formation of C-N bond between WPF6-oxy-F and GO. As a result, the efficiency of OSCs was dramatically enhanced ~ 6.10% by introducing irradiated WPF-6-oxy-F-GO as a HTL. WPF-6-oxy-F-GO is a sufficient candidate for HTL to facilitate the low-cost and high efficiency OSCs.

• Nuclear Engineering and Technology
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• v.16 no.3
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• pp.141-154
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• 1984

An analysis is presented of key phenomena and scenario which imply some general trends for beyond design-basis-accident in Kori-1 PWR dry containment. The study covers a wide range of severe accident sequences initiated by small break LOCA. The MARCH computer code, with KAERI modifications was used in this analysis. The major emphasis of the paper are two folds, 1) the phenomenologic understanding of severe accident and 2) a study of H2 combustion and debris/ water interactions in a specific small break LOCA for Kori-1 plant. The sensitivity studies for the specific plant data and thermal interaction modelings used in the SASA were performed. The results show that if hydrogen burning does occur at low concentration, the resulting peak pressure does not exceed the design value, while the lower concentration assumption results in repeated burning due to the continuing H$_2$ generation. For debris/water interaction, the particle size has no effect on the magnitude of peak pressure for the amount of water assumed to be in the reactor cavity. But, the occurrence of peak pressure is considerably delayed in case of using the dryout correlation. The peak containment pressure predicted from the hydrogen combustion and steam pressure spite during full core meltdown scenario does not present a severe threat to the containment integrity.

• Korean Journal of Optics and Photonics
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• v.17 no.5
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• pp.383-390
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• 2006

The SharkHartmann wavefront sensors are the most popular devices to measure wavefront in the field of adaptive optics. The Shack-Hartmann sensors measure the centroids of spot irradiance distribution formed by each corresponding micro-lens. The centroids are linearly proportional to the local mean slopes of the wavefront defined within the corresponding sub-aperture. The wavefront is then reconstructed from the evaluated local mean slopes. The uncertainty of the Shack-Hartmann sensor is caused by various factors including the detector noise, the limited size of the detector, the magnitude and profile of spot irradiance distribution, etc. This paper investigates the noise propagation in two major centroid evaluation algorithms through computer simulation; 1st order moments of the irradiance algorithms i.e. center of gravity algorithm, and correlation algorithm. First, the center of gravity algorithm is shown to have relatively large dependence on the magnitudes of noises and the shape & size of irradiance sidelobes, whose effects are also shown to be minimized by optimal thresholding. Second, the correlation algorithm is shown to be robust over those effects, while its measurement accuracy is vulnerable to the size variation of the reference spot. The investigation is finally confirmed by experimental measurements of defocus wavefront aberrations using a Shack-Hartmann sensor using those two algorithms.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.18-25
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• 2005

We derive refraction statics for seismic data recorded in a hard rock terrain, in which there are large and rapid variations in the depth of weathering. The statics corrections range from less than 10 ms to more than 70 ms, often over distances as short as 12 receiver intervals. This study is another demonstration of the importance in obtaining accurate initial refraction models of the weathering in hard rock terrains in which automatic residual statics may fail. We show that the statics values computed with a simple model of the weathering using the Generalized Reciprocal Method (GRM) and the Refraction Convolution Section (RCS) are comparable in accuracy to those computed with a more complex model of the weathering, using least-mean-squares inversion with the conjugate gradient algorithm (Taner et al., 1998). The differences in statics values between the GRM model and that of Taner et al. (1998) systematically vary from an average of 2ms to 4ms over a distance of 8.8 km. The differences between these two refraction models and the final statics model, which includes the automatic residual values, are generally less than 5 ms. The residuals for the GRM model are frequently less than those for the model of Taner et al. (1998). The RCS statics are picked approximately 10 ms later, but their relative accuracy is comparable to that of the GRM statics. The residual statics values show a general correlation with the refraction statics values, and they can be reduced in magnitude by using a lower average seismic velocity in the weathering. These results suggest that inaccurate average seismic velocities in the weathered layer may often be a source of short-wavelength statics, rather than any shortcomings with the inversion algorithms in determining averaged delay times from the traveltimes.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.229-235
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• 2011

In order to manage a sewer system effectively, flow conditions such as flux, water quality, Infiltration and Inflow (I/I), Combined Sewer Overflows (CSOs), etc need to be monitored on a regular base. Therefore, in sewer networks, a monitoring is so important to prevent the river disaster. Monitoring all nodes of an entire sewer system is not necessary and cost-prohibitive. Water quality monitoring points that can represent a sewer system should be selected in a economical manner. There is no a standard for the selection of monitoring points and the quantitative analysis of the observed data has not been applied in sewer system. In this study, the entropy method was applied for a sewer network to evaluate and determine the optimal water quality monitoring points using genetic algorithm. The entropy method allows to analyze the observed data for the pattern and magnitude of temporal water quality change. Since water quality measurement usually accompanies with flow measurement, a set of installation locations of flowmeters was chosen as decision variables in this study.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1061-1069
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• 2021

Recently, the magnitude and frequency of extreme heavy rains and localized heavy rains have increased due to abnormal climate, which caused increased flood damage in river basin. As a result, the nonlinearity of the hydrological system of rivers or basins is increasing, and there is a limitation in that the lead time is insufficient to predict the water level using the existing physical-based hydrological model. This study predicted the water level at Ulsan (Taehwagyo) with a lead time of 0, 1, 2, 3, 6, 12 hours by applying deep learning techniques based on Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and evaluated the prediction accuracy. As a result, DNN model using the sliding window concept showed the highest accuracy with a correlation coefficient of 0.97 and RMSE of 0.82 m. If deep learning-based water level prediction using a DNN model is performed in the future, high prediction accuracy and sufficient lead time can be secured than water level prediction using existing physical-based hydrological models.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.8-15
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• 2021

This paper describes a design method using an optimization technique to satisfy the longitudinal handling quality of high maneuverable jet aircraft. The dynamic inversion technique was applied to the target aircraft, and the control gain optimization satisfied the longitudinal short-period handling quality, however, the stability margin was not considered. If the stability margin is not satisfied, it is necessary to directly readjust the gains through trial and error methods for improvement. To improve this, an additional compensator and an optimization constraint were added to the control gain optimization procedure. In addition, the degree of handling quality satisfaction with the optimization result was reevaluated, and additional control evaluation criteria for the convergence of the time response and the steady state error that the flight performance requirement set as the optimization constraint cannot be reflected, and the results are described.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.927-938
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• 2021

A similar magnitude of non-gravitational perturbations are act on the formation flying low earth orbit satellites with a certain time difference. Using this temporal correlation, the non-gravity acceleration of the low earth orbiting satellites can be transferred for the othersatellites. There is a period in which the accelerometer data of one satellite is unavailable for GRACE and GRACE-FO satellites. In this case, the accelerometer data transplant method described above is officially used to recover the accelerometer data at the Jet Propulsion Laboratory (JPL). In this paper, we proposed a model for predicting accelerometer data of formation flying low earth orbit satellites using a neural network (NN) model to improve the estimation accuracy of the transplant method. Although the transplant method cannot reflect the satellite's position and space environmental factors, the NN model can use them as model inputs to increase the prediction accuracy. A prediction test of an accelerometer data using NN model was performed for one month, and the prediction accuracy was compared with the transplant method. The NN model outperformsthe transplant method with 55.0% and 40.1% error reduction in the along-track and radial directions, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.1-16
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• 2021

Global warming due to climate change is expected to significantly affect the hydrological cycle of agriculture. Therefore, in order to predict the magnitude of climate impact on agricultural water resources in the future, it is necessary to estimate the water demand for irrigation as the climate change. This study aimed at evaluating the future changes in water demand for irrigation under two Shared Socioeconomic Pathways (SSPs) (SSP2-4.5 and SSP5-8.5) scenarios for paddy rice in Gimje, South Korea. The APEX-Paddy model developed for the simulation of paddy environment was used. The model was calibrated and validated using the H₂O flux observation data by the eddy covariance system installed at the field. Sixteen General Circulation Models (GCMs) collected from the Climate Model Intercomparison Project phase 6 (CMIP6) and downscaled using Simple Quantile Mapping (SQM) were used. The future climate data obtained were subjected to APEX-Paddy model simulation to evaluate the future water demand for irrigation at the paddy field. Changes in water demand for irrigation were evaluated for Near-future-NF (2011-2040), Mid-future-MF (2041-2070), and Far-future-FF (2071-2100) by comparing with historical data (1981-2010). The result revealed that, water demand for irrigation would increase by 2.3%, 4.8%, and 7.5% for NF, MF and FF respectively under SSP2-4.5 as compared to the historical demand. Under SSP5-8.5, the water demand for irrigation will worsen by 1.6%, 5.7%, 9.7%, for NF, MF and FF respectively. The increasing water demand for irrigating paddy field into the future is due to increasing evapotranspiration resulting from rising daily mean temperatures and solar radiation under the changing climate.