• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.11-20
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• 2020

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture data assimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soil moisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulates the soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validated and evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (Automated Synoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. The derived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001 to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements and Sentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.191-200
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• 1993

This study introduced speical characteristics of an epidemiologic study on blood pressure and compared several statistical methods for evaluating the tracking phenomenon of blood pressure for Korean children. While correlation coefficients adjusted for measurement error are commonly used for the evaluation of tracking, it is hard to interpretate the results when correlation functions for lag-difference are not monotonous. McMahan defined a tracking as maintenance of relative rank over time and calculated tracking index usng growth curve model. The tracking index in McMahan's model is complicate to calculate, and it is hard to determine the degree of growth curve parameter. Blomqvist showed the relationship between the rate of change and the initial value. This concept could be extended for the evaluation of tracking. However, it is not so easy to interpretate the estimates in his model when those are non-positive.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.531-537
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• 2010

Real-time Aerial Monitoring System performs the rapid mapping in an emergency situation so that the geoinformation could be constructed in near real time. In this system, the position and attitude information from GPS/INS integration algorithm is used to perform the aerial triangulation(AT) without GCPs. Therefore, if we obtain Exterior Orientation(EO) estimates from AT sequentially, EO are used as the measurements in the Kalman filter. In this study, we simulate the GPS/IMS/Image data for an UAV-based aerial monitoring system and compare the GPS/INS/AT with and without from AT. Comparative analysis showed that result from the GPS/INS/AT with EO update is more accurate than without the update. However, when the vehicle turns, the position error significantly increases which need more analysis in the future.

• Journal of Advanced Navigation Technology
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• v.19 no.2
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• pp.123-132
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• 2015

Among various fields of the CNS/ATM, the surveillance field which includes ADS-B system, MLAT system, and WAM system is implemented. These next generation systems provide superior performance in tracking aircrafts. However, They still have error. In this paper, filtering algorithm is proposed in order to enhance aircraft tracking performance of ADS-B, MLAT, and WAM systems. The proposed method is a Robust Interacting Multiple Model filter, called Robust IMM filter, that improves IMM filter. The Robust IMM filter can not only improves the aircraft tracking performance but also track aircraft continually using estimates calculated from the filter when data losses occur. The simulation results of the proposed aircraft tracking methods show that the filtering data provides a better performance up to an average of 19.21%.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.696-700
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• 2007

A new method that improves the performance of the phonetic transcription based speech recognition system is presented with the speaker-independent phonetic recognizer. Since SI phoneme HMM based speech recognition system uses only the phoneme transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the speaker dependent system due to the phoneme recognition errors generated from using SI models. A new training method that iteratively estimates the phonetic transcription and transformation vectors is presented to reduce the mismatch between the training utterances and a set of SI models using speaker adaptation techniques. For speaker adaptation the stochastic matching methods are used to estimate the transformation vectors. The experiments performed over actual telephone line shows that a reduction of about 45% in the error rates could be achieved as compared to the conventional method.

• Smart Structures and Systems
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• v.16 no.1
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• pp.163-181
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• 2015

This research investigates the structural health monitoring of nonlinear structures after a major seismic event. It considers the identification of flag-shaped or pinched hysteresis behavior in response to structures as a more general case of a normal hysteresis curve without pinching. The method is based on the overall least squares methods and the log likelihood ratio test. In particular, the structural response is divided into different loading and unloading sub-half cycles. The overall least squares analysis is first implemented to obtain the minimum residual mean square estimates of structural parameters for each sub-half cycle with the number of segments assumed. The log likelihood ratio test is used to assess the likelihood of these nonlinear segments being true representations in the presence of noise and model error. The resulting regression coefficients for identified segmented regression models are finally used to obtain stiffness, yielding deformation and energy dissipation parameters. The performance of the method is illustrated using a single degree of freedom system and a suite of 20 earthquake records. RMS noise of 5%, 10%, 15% and 20% is added to the response data to assess the robustness of the identification routine. The proposed method is computationally efficient and accurate in identifying the damage parameters within 10% average of the known values even with 20% added noise. The method requires no user input and could thus be automated and performed in real-time for each sub-half cycle, with results available effectively immediately after an event as well as during an event, if required.

• 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.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.20-26
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• 2021

In this paper, we studied the rotation angle estimation methods required for image alignment in an image recognition environment. In particular, a rotation angle estimation method applicable to a low specification embedded-based environment was proposed and compared with the existing method using complex moment. The proposed method estimates the rotation angle through similarity mathcing of the 1D projection profile along the radial axis after converting an image into polar coordinates. In addition, it is also possible to select a method of using vector sum of the projection profile, which more simplifies the calculation. Through experiments conducted on binary pattern images and gray-scale images, it was shown that the estimation error of the proposed method is not significantly different from that of complex moment-based method and requires less computation and system resources. For future expansion, a study on how to match the rotation center in gray-scale images will be needed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.127-134
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• 2021

This study aims to determine the role of innovative capabilities as a mediator in analyzing entrepreneurial marketing's effect on sustainable competitive advantage in food and beverage micro-, small-, and medium- enterprises (MSMEs). Data was obtained from a food and beverage store manager in Tangerang City, comprising 119 samples. Furthermore, the G⁎Power, a tool used to calculate statistical power analysis for various t-tests, F tests, χ2 tests, z tests, and several exact tests, was used to determine the number of research samples, the α error probability of 5%, and 3 variables. The data collection method used questionnaires with Likert Scale 1-5 to indicate strongly disagree to strongly agree. To analyze data, we used Path Analysis supported by SmartPLS statistics software. Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It aims to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. The data processing process took place in two stages, namely the estimation model testing with validity and reliability, and the structural model testing to decide the impact or correlation between variables utilizing the t-test. The result showed a positive and significant effect of entrepreneurial marketing to innovative capability and competitive advantage through the innovative capability of MSMEs.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.