Earticle

본 연구에서는 다중최적화기법을 이용하여 2가지 수문학적 과정을 통하여 유출량을 산정하는 수문모형의 모형 최적화를 시도하였으며, 수문모형으로는 융설량과 유출량을 동시에 산정할 수 있는 분포형 수문모형인 HL-RDHM을 이용하였다. 대상유역으로는 융설량 자료를 수집할 수 있는 미국 콜로라도의 Durango River 유역을 선정하였다. 다중최적화기법으로는 MOSCEM을 활용하였으며, 융설과 관련된 매개변수 5개와 유출에 관련된 매개변수 13개를 선정하여 매개변수 보정과 수문모형 최적화를 시도하였다. 모형 최적화를 위해 2004 – 2005년의 자료가 활용되었고, 2001 – 2004년 자료를 이용하여 검증하였다. 융설량과 유출량을 동시에 최적화함으로써 RMSE 기준으로, 3개의 SNOTEL 지점에서 초기해에 의한 모의치 보다 7% - 40%까지 RMSE 오차를 줄일 수 있었고, 유출구의 USGS 관측점에서 초기해에 비해 약 40% 값이 개선됨을 확인하였다.

In this study, the multi-objective optimization method is attemped to optimize the hydrological model to estimate the runoff through two hydrological processes. HL-RDHM, a distributed hydrological model that can simultaneously estimate the amount of snowfall and runoff, was used as the distributed hydrological model. The Durango River basin in Colorado, USA, was selected as the watershed. MOSCEM was used as a multi-objective optimization method and parameter calibration and hydrologic model optimization were tried by selecting 5 parameters related to snow melting and 13 parameters related to runoff. Data from 2004 to 2005 were used to optimize the model and verified using data from 2001 to 2004. By optimizing both the amount of snow and the amount of runoff, the RMSE error can be reduced from 7% to 40% of the simulation value based on the initial solution at three SNOTEL points based on the RMSE. The USGS observation point of the outflow is improved about 40%.

Most carcass and meat quality traits are moderate to highly heritable, indicating that they can be improved through selection. Genetic evaluation for these types of traits is performed using performance data obtained from commercial and progeny testing evaluation. The performance data from commercial farms are available in large volume, however, some drawbacks have been observed. The drawback of the commercial data is mainly due to sorting of animals based on live weight prior to slaughter, and this could lead to bias in the genetic evaluation of later measured traits such as carcass traits. The current study has two components to address the drawback of the commercial data. The first component of the study aimed to estimate genetic parameters for carcass and meat quality traits in Korean Hanwoo cattle using a large sample size of industry-based carcass performance records (n = 469,002). The second component of the study aimed to describe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequently measured traits. To demonstrate our objectives, we used real performance data to estimate genetic parameters and simulated data was used to assess the bias in genetic evaluation. The results of our first study showed that commercial data obtained from slaughterhouses is a potential source of carcass performance data and useful for genetic evaluation of carcass traits to improve beef cattle performance. However, we observed some harvesting effect which leads to bias in genetic evaluation of carcass traits. This is mainly due to the selection of animal based on their body weight before arrival to slaughterhouse. Overall, the non-random allocation of animals into a contemporary group leads to a biased estimated breeding value in genetic evaluation, the severity of which increases when the evaluation traits are highly correlated.

This study analyzes the three different estimation algorithms for recovering item parameters for the compensatory multidimensional IRT (MIRT) models. In particular, two- and four-dimensional models were investigated with different degrees of correlation between latent traits. The standards such as bias, standard error, and root mean square error were used to evaluate the recovery of item parameters for each program. The results indicated that in most conditions, Metropolis-Hasting Robbins-Monro (MH-RM) outperformed full information item factor analysis (FIIFA) and bivariate information item factor analysis (BIIFA) for a-parameters except for the independent and very low inter-trait correlation conditions where BIIFA outperformed the other algorithms. However, the MH-RM algorithm consistently produced the highest empirical standard errors compared to the other two methods for all conditions. FIIFA performed at a higher standard than BIIFA for a-parameters with moderately correlated latent traits. BIIFA is more suitable for a-parameters, especially when the levels of latent traits' independence or correlation are very low, and it is more suitable for d-parameters regardless of inter-trait correlations in the four-dimensional models. Overall, three estimation methods provided more accurate a- and d-parameter as the number of examinees increased, and less accurate a-parameter occurred as the inter-trait correlation increased. The inter-trait correlation condition did not have a dramatic impact on the recovery of d-parameter across all three algorithms.

The stability and control derivatives of DURUMI-lI UAV using the flight test are obtained. The flight test data is gathered from the normal flight condition (normal mode) and the flight condition assumed as the right elevator fixed (fault mode). Using real-time parameter estimation techniques, applied to Fourier transform regression method, simulates the aircraft motion. From the result, the fault of control surface is to be detected. In this paper, the results of the real- time parameter estimation techniques are compared with the results of the Advanced Aircraft Analysis (AAA). Using the aerodynamic derivatives, it provides the base line of normal/failure for the control surface by using the on-line parameter estimation of Fourier transform regression. In flight, this approach maybe helpful to detect and isolate the fault of primary control surface. It is explained how to perform the flight condition assumed as the right elevator fixed in the flight test. Also, it is mentioned how to switch between the normal flight condition and the assumed fault flight condition.

In this paper, a specific type of incomplete data in Wi-Fi fingerprinting based indoor positioning systems (WF-IPS) is presented: censored and dropped mixture data. For fitting this type of data, a censored and dropped Gaussian Mixture Model (CD-GMM) was proposed. Further, an extended version of the Expectation-Maximization (EM) algorithm is developed for estimating parameters of this model. Simulation results demonstrate the effectiveness of the proposed method for parameter estimation.

The transmission capacity of transmission lines is affected by environmental parameters such as ambient temperature, wind speed, wind direction and so on. The environmental parameters can be measured by the installed measuring devices. However, it is impossible to install the environmental measuring devices throughout the line, especially considering economic cost of power grid. Taking into account the limited number of measuring devices and the distribution characteristics of environment parameters and transmission lines, this paper first studies the environmental parameter estimating method of inverse distance weighted interpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEE standard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect the load capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line is realized by using the data obtained from China meteorological data network. The cost of the environmental measurement device is reduced, and the accuracy of dynamic rating is improved.

The parameters of transmission lines have an influence on the electromagnetic environment surrounding the line. This paper proposes a method based on phasor measurement unit (PMU) and supervisory control and data acquisition (SCADA) to achieve online estimation of transmission line full parameters, such as resistance, reactance and susceptance. The proposed full parameter estimation method is compared with the traditional method of estimating resistance independently based on SCADA system. Then, the electromagnetic environment is analyzed based on the different parameter estimation methods. The example results illustrate that online estimation of transmission line full parameters is more accurate in the analysis of electromagnetic environment, which further confirms its necessity and significance in engineering application.

As the requirement on the quality of the system has increased, the reliability is very important part in terms of enhance stability and to provide high quality services to customers. Many statistical models have been developed in the past years for the estimation of software reliability. We consider the functions for NHPP software reliability model and time series model in software failure data. We estimate parameters for the proposed models from three data sets. The values of SSE and MSE is presented from three data sets. We compare the predicted number of faults with the actual three data sets using the NHPP software reliability model and time series model.

We consider the mean value function for NHPP software reliability model and time series regression model in software failure data. We estimate parameters for the proposed models from two data sets. The values of SSE and MSE is presented from two data sets. We compare the predicted number of faults with the actual two data sets using the mean value function and regression curve.

The skew-normal distribution has gained widespread application across various domains due to its flexibility in modeling asymmetric data. However, accurately estimating its parameters-particularly the shape parameter-as well as those of its extended families such as the truncated or multivariate skew-normal distributions, remains a significant challenge. This paper presents a brief theoretical review of two primary parameter estimation methods: Maximum Likelihood Estimation (MLE) and Weighted Moment Estimation (WMM). For the MLE approach, we further investigate the effectiveness of reparameterization techniques in enhancing numerical stability. The advantages, limitations, and computational behavior of both methods are thoroughly examined. Results indicate that reparameterization substantially improves the stability of MLE, while WMM demonstrates superior performance in scenarios involving small sample sizes or high skewness. In contrast, MLE may underperform in complex or extreme data settings. These findings provide valuable guidance for researchers in selecting appropriate estimation strategies and lay the groundwork for the development of more robust estimation methods in future studies.

Determining design rainfall is the first step to plan an agricultural drainage facility. The objective of this study is to evaluate whether the current method for parameter estimation is reasonable for computing the design rainfall. The current Gumbel-Kendall (G-K) method was compared with two other methods which are Gumbel-Chow (G-C) method and Probability weighted moment (PWM). Hourly rainfall data were acquired from the 60 ASOS (Automated Synoptic Observing System) stations across the nation. For the goodness-of-fit test, this study used chi-squared (𝛘²) and Kolmogorov-Smirnov (K-S) test. When using G-K method, 𝛘² statistics of 18 stations exceeded the critical value (𝑥²_a=0.05,df=4=9.4877) and 10, 3 stations for G-C method, PWM method respectively. For K-S test, none of the stations exceeded the critical value (D^a=0.05_n=0.19838). However, G-K method showed the worst performances in both tests compared to other methods. Subsequently, this study computed design rainfall of 48-hour duration in 60 ASOS stations. G-K method showed 5.6 and 6.4% higher average design rainfall and 15.2 and 24.6% higher variance compared to G-C and PWM methods. In short, G-K showed the worst performance in goodness-of-fit tests and showed higher design rainfall with the least robustness. Likewise, considering the basic assumptions of the design rainfall estimation, G-K is not an appropriate method for the practical use. This study can be referenced and helpful when revising the agricultural drainage standards.

In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

본 연구에서는 Weibull 확률분포함수의 매개변수 추정방법을 적용하였으며, 재현기간별 신뢰한계를 구하기 위한 점근분산식(漸近分散式)을 유도하였다. 각 과정은 기존의 모멘트법, 최우도법, 확률가중 모멘트법(Probability weighted moments)개념에 기초하여 유도하였으며, 유도된 식들을 실제 홍수자료에 적용하였다.

For the three parameter Weibull distribution, the parameter estimation techniques are applied and the asymptotic variances of the quantile to obtain the confidence limits for a given return period are derived. Three estimation techniques are used for these purposes: the methods of moments, maximum likelihood and probability weighted moments. The three parameter Weibull distribution as a flood frequency model is applied to actual flood data.

본 연구에서는 기존의 대수(對數)형태인 대수(對數)-Gumbel 확률분포함수를 변환하여 새로운 형태의 대수(對數)-Gumbel 확률분포함수를 정립하였다. 이 분포함수를 이용하여 모멘트법, 최우도법, 확률가중모멘트법(Probability weighted moments)에 기초한 매개변수 추정과정을 유도하였으며, 또한 재현기간별 신뢰한계를 구하기 위하여 각각의 매개변수 추정법에 대한 점근분산식(漸近分散式)을 유도하였다. 아울러 유도된 식들을 실제 자료에 적용하였다.

The log-Gumbel distribution in real space is defined by transforming the conventional log-Gumbel distribution in log space. For this model, the parameter estimation techniques are applied based on the methods of moments, maximum likelihood and probability weighted moments. The asymptotic variances of estimator of the quantiles for each estimation method are derived to find the confidence limits for a given return period. Finally, the log-Gumbel model is applied to actual flood data to estimate the parameters, quantiles and confidence limits.

The genetic parameters used in National Hanwoo Genetic Evaluation(NHGE) were needed to be monitored and updated periodically for accounting any possible changes in population parameters due to selection and environmental changes. Genetic parameters were estimated with single and two-trait models with MTDFREML package using 2,791 carcass records of steers collected from Hanwoo Progeny Test Program(HPTP). Single and two-trait models gave similar parameter estimates for all traits. The heritability estimates from single and two-trait models for carcass weight(CW), dressing percentage(DP), eye muscle area(EMA), back fat thickness(BFT) and marbling score(MS) were 0.30, 0.30, 0.37, 0.44 and 0.44, respectively. The heritability estimates for all the traits except BFT were slightly lower than those used in NHGE but seemed to be within the acceptable ranges. However, further monitoring is needed because the data might not have fully reflected the changes such as carcass grading standards in performance testing program. In order to shift statistical model of NHGE from single trait model to multiple-trait model, the genetic correlations between carcass traits were estimated with pairwise two-trait models. The genetic correlation coefficients between CW and DP, between CW and EMA, between CW and BFT and between CW and MS were 0.44, 0.63, 0.17 and 0.06, respectively. Those between DP and EMA, between DP and BFT and between DP and MS were 0.29, 0.40 and 0.20. Those between EMA and BFT and between EMA and MS were －0.24 and 0.15, respectively. The genetic correlation coefficient between BFT and MS was 0.03.

본 연구는 농협중앙회 가축개량사업소가 보유하고 있는 1998년부터 2005년까지 실시한 한우 당대검정 대상우 2,532두의 자료와 1996년부터 2002년까지 실시한 한우 후대검정 대상우 1,819두의 자료를 이용하여 당대 및 후대의 형질을 다형질 개체모형을 이용하여 유전모수를 추정하였다. 적합한 통계분석모형을 찾기 위하여 각 형질별로 회귀분석을 통한 변수선택방법으로 고정효과와 공변량을 결정하고, MTDFREML 패키지를 이용하여 유전모수를 추정하였다. 분석형질은 등지방두께, 도체중, 도체율, 등심단면적, 근내지방도 및 12개월령 체중으로, 이들의 유전력은 각각 0.51, 0.32, 0.27, 0.33, 0.50 및 0.26로 나타났다. 한편 유전평가에서 제외되었던 등지방두께 및 도체율의 유전력이 각각 0.51 및 0.27으로 추정되어 이에 대한 선발이 가능할 것으로 나타났다. 특히 등지방두께는 현 육량지수 산출식에서 그 영향력이 매우 크므로 한우보증씨수소 선발지수에 포함하는 방안을 고려할 필요가 있을 것으로 사료된다. 도체형질간의 유전상관은 등지방두께와 등심단면적을 제외하고 모두 양의 상관을 나타났다. 그러나, 12개월령 보정체중과 도체율 및 근내지방도의 유전상관은 각각 0.09 및 0.27으로 나타나 현행 한우개량체계에서 처럼 당대검정우를 12개월령 체중으로 선발할 경우 등심단면적과 근내지방도가 우수한 개체가 탈락할 가능성이 있는 것으로 나타나 이의 보완을 위한 혈통지수의 활용방안이나 초음파단층촬영기술의 이용방법에 대한 추가연구가 필요한 것으로 사료된다.

author : D. H. Baik, Dept. of Animal Resources and Biotechnology, Chonbuk National University., Duckjin-dong, Duckjin-gu, Jeonju-si, Jeollabuk-do 561-756 Rep. of Korea.Tel: +82-63-270-2609 E-mail: baik@chonbuk.ac.kr

Model structure of solenoid valve in the automatic transmission is determined as 5th order system by the signal error test. For determining parameter of the solenoid valve, parameters in time discrete model are searched by the least square method. By bilinear transform, we have found the model of solenoid valve in s domain. Afterward, experimental output data is compared with simulated output data by MATLAB having identified parameter. As the result, experimental data is agreed with simulated data very well.

By using the signal error test, model structure of total antilock braking system consisting of electromagnetic system and hydraulic system is determined as 9th order system. For determining parameters of the ABS, using time discrete model of parametric method, parameters in time discrete model are searched by least square method. By bilinear transform, we have found the model of ABS in s domain. Afterward, experimental output data is compared with simulated output data by MATLAB haying identified parameter. As the result, experimental data is agreed with simulated data very well.

By using the test of signal error, model structure of an electrohydraulic servo system is determined. For determining parameter of the electrohydraulic servo system, using time discrete model of parametric method, parameters in time discrete model are searched by the least square method. By bilinear transform, we have found the model of electrohydraulic servo system in s domain. Afterwards, we have compared experimental data with simulation data by MATLAB having the identified parameter. As the result, experimental data is agreed with simulation data very well.