• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Journal of the Korean Society of Propulsion Engineers
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• v.18 no.5
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• pp.62-69
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• 2014

Infrared signature of rocket plume plays an important role for detection, recognition, tracking and minimzing for low observability. Infrared signatures of rocket plume with reduced smoke propellant and smokeless propellant are measured. In order to estimate the infrared signature of rocket plume, CFD analysis for flow structure of plume is performed, and layered integration method for estimating of infrared signature is used. Numerical and experimental results were in good agreement. Both propellants had similar infrared signature. Strong peak at $4.3{\mu}m$ region in the experimental results is appeared due to experimental error arising from the calibration procedure.

• Smart Structures and Systems
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• v.7 no.5
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• pp.379-392
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• 2011

This paper presents a multi-functional system, consisting of a magnetorheological (MR) damper and an electromagnetic induction (EMI) device, and its applications in stay cables. The proposed system is capable of offering multiple functions: (1) mitigating excessive vibrations of cables, (2) estimating cable tension, and (3) harvesting energy for wireless sensors used health monitoring of cable-stayed bridges. In the proposed system, the EMI device, consisting of permanent magnets and a solenoid coil, can converts vibration energy into electrical energy (i.e., induced emf); hence, it acts as an energy harvesting system. Moreover, the cable tension can be estimated by using the emf signals obtained from the EMI device. In addition, the MR damper, whose damping property is controlled by the harvested energy from the EMI device, can effectively reduce excessive cable vibrations. In this study, the multi-functionality of the proposed system is experimentally evaluated by conducting a shaking table test as well as a full-scale stay cable in a laboratory setting. In the shaking table experiment, the energy harvesting capability of the EMI device for wireless sensor nodes is investigated. The performance on the cable tension estimation and the vibration mitigation are evaluated using the full-scale cable test setup. The test results show that the proposed system can sufficiently generate and store the electricity for operating a wireless sensor node twice per day, significantly alleviate vibration of a stay cable (by providing about 20% larger damping compared to the passive optimal case), and estimate the cable tension accurately within a 2.5% error.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.5
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• pp.647-655
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• 2010

In this paper, a numerical study is carried out for super-pipe, flat plate and axisymmetric body flows to investigate a validity of using wall function and high $y_1^+$ in calculation of high Reynolds number flow. The velocity profiles in boundary layer agree well with the law of the wall. And it is found that the range of $y^+$��which validated the logarithmic law of the wall grows with increasing Reynolds number. From the result, an equation is suggested that can be used to estimate a maximum $y^+$ value of validity of the log law. And the slope(1/$\kappa$) of the log region of the numerical result is larger than that of experimental data. On the other hand, as $y_1^+$ is increasing, both the friction and the pressure resistances tend to increase finely. When using $y_1^+$ value beyond the range of log law, the surface shear stress shows a significant error and the pressure resistance increases rapidly. However, when using $y_1^+$ value in the range, the computational result is reasonable. From this study, the use of the wall function with high value of $y_1^+$ can be justified for a full scale Reynolds number ship flow.

• Journal of the Korean Solar Energy Society
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• v.38 no.5
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• pp.11-25
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• 2018

The Ln-least method is commonly used to estimate the Weibull parameters from the observed wind speed data. In previous studies, the bin method has been used to calculate the cumulative frequency distribution for the Ln-least method. The purpose of this study is to obtain better performance in the Ln-least method by applying probability plotting position(PPP) instead of the bin method. Two types of the wind speed data were used for the analysis. One was the observed wind speed data taken from three sites with different topographical conditions. The other was the virtual wind speed data which were statistically generated by a random variable with known Weibull parameters. Also, ten types of PPP formulas were applied which were Hazen, California, Weibull, Blom, Gringorten, Chegodayev, Cunnane, Tukey, Beard and Median. In addition, in order to suggest the most suitable PPP formula for estimating Weibull parameters, two accuracy tests, the root mean square error(RMSE) and $R^2$ tests, were performed. As a result, all of PPPs showed better performances than the bin method and the best PPP was the Hazen formula. In the RMSE test, compared with the bin method, the Hazen formula increased estimation performance by 38.2% for the observed wind speed data and by 37.0% for the virtual wind speed data. For the $R^2$ test, the Hazen formula improved the performance by 1.2% and 2.7%, respectively. In addition, the performance of the PPP depended on the frequency of low wind speeds and wind speed variability.

• Korea journal of population studies
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• v.11 no.1
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• pp.1-13
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• 1988

This study examines the sampling bias that may have resulted from the large number of missing observations. Despite well-designed and reliable sampling procedures, the observed sample values in DSFH(Demographic Survey on Changes in Family and Household Structure, Japan) included many missing observations. The head administerd survey method of DSFH resulted in a large number of missing observations regarding characteristics of elderly non-head parents and their children. In addition, the response probability of a particular item in DSFH significantly differs by characteristics of elderly parents and their children. Furthermore, missing observations of many items occurred simultaneously. This complex pattern of missing observations critically limits the ability to produce an unbiased analysis. First, the large number of missing observations is likely to cause a misleading estimate of the standard error. Even worse, the possible dependency of missing observations on their latent values is likely to produce biased estimates of covariates. Two models are employed to solve the possible inference biases. First, EM algorithm is used to infer the missing values based on the knowledge of the association between the observed values and other covariates. Second, a selection model was employed given the suspicion that the probability of missing observations of proximity depends on its unobserved outcome.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.5
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• pp.429-434
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• 2007

This study was conducted to estimate site productivity of Quercus acutissima and Quercus mongolica by four forest climatic zones. We used site environmental variables (28 geographical and pedological factors) and site index as a site productivity indicator from nation-wide 23,315 stands. Based on multiple regression analysis between site index and major environmental variables, the best-fit multivaliate models were made by each species and forest climatic zone. Most of site index prediction models by species were regressed with seven to eight factors, including altitude, relief, soil depth, and soil moisture etc. For those models, three evaluation statistics such as mean difference, standard deviation of difference, and standard error of difference were applied to the test data set for the validation of the results. According to the evaluation statistics, it was found that the models by climatic zones and species fitted well to the test data set with relatively low bias and variation. Also having above middle of site index range, total area of productive sites for the two Quercus spp. estimated by those models would be about 6% of total forest area. Northern temperate forest zone and central temperate forest zone had more productive area than southern temperate forest zone and warm temperate forest zone. As a result, it was concluded that the regressive prediction with site environmental variables by climatic zones and species had enough estimation capability of forest site productivity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.43-48
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• 2021

We propose 2-channel filter bank method instead of FFT method to decrease sub-channel interference for using of efficient frequency resource in MC-CDMA method. Since a prototype filter of filter bank having wavelet characteristic is designed having more less side-lobe, the nearest co-channel interference and inter symbol interference are decreased efficiently. Since the spreading signal of suggesting MC-CDMA system is being demanded for less chip rate and is not being considering for autocorrelation characteristic, the Walsh code can be used as a optimal orthogonal signal set. We consider bit error rate and signal to noise ratio to estimate the performance of suggested system on condition that white noise channel and arbitrary sinusoidal jammer are existing. As a result of comparing to traditional FFT-based MC-CDMA simulation result, our suggested system has shown better performance than traditional MC-CDMA method on the side of minimizing interference effect.

• Journal of Korean Society on Water Environment
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• v.37 no.2
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• pp.103-113
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• 2021

In this study, an Excel-based model (ROADMOD) was developed to estimate pollutant loading from the road and evaluate BMPs. ROADMOD employs the Chezy-Manning equation and empirical expression for estimating surface runoff, and power function for pollutant buildup, and exponential function for pollutant washoff in SWMM. The results of model calibration for buildup and washoff using observed data revealed a good match between the simulation results and the observed data. The long-term surface runoff and sediment simulated by ROADMOD demonstrated a good match with those by SWMM with 2 ~ 14% of relative error. The shorter sweeping interval (within 8 days) remarkably decreased sediment loads from the road. It was found that the effect of reducing sediment loads from the road was greatly affected not only by the sweeping interval but also by sweeping on the day before a rainfall event. The 48% of removal efficiency of sediment loads from the road was achieved with 26 times of road sweeping per year when sweeping was performed on the day before the rainfall event. A 4-day sweeping interval showed similar removal efficiency (48%) with 96 times of sweeping per year. It is considered that the road sweeping on the day before a rainfall event could maximize the effect of reducing the non-point source pollution from the road with minimization of the number of road sweeping. So, the road sweeping on the day before a rainfall event can be considered as one of the useful and best management practices (BMPs) on road.