• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.119-133
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• 2014

The study adopted extended Kalman filter technique in an effort to predict Z-R relationship parameter as a stable value in real-time. Toward this end, a parameter estimation model was established based on extended Kalman filter in consideration of non-linearity of Z-R relationship. A state-space model was established based on a study that was conducted by Adamowski and Muir (1989). Two parameters of Z-R relationship were set as state variables of the state-space model. As a result, a stable model where a divergence of Kalman gain and state variables are not generated was established. It is noteworthy that overestimated or underestimated parameters based on a conventional method were filtered and removed. As application of inappropriate parameters might cause physically unrealistic rain rate estimation, it can be more effective in terms of quantitative precipitation estimation. As a result of estimation on radar rainfall based on parameters predicted with the extended Kalman filter, the mean field bias correction factor turned out to be around 1.0 indicating that there was a minor difference from the gauge rain rate without the mean field bias correction. In addition, it turned out that it was possible to conduct more accurate estimation on radar rainfall compared to the conventional method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.122-130
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• 2021

A wind-speed estimation at the arbitrary elevations is key component for the design of the offshore wind energy structures and the computation of the wind-wave generation. However, the wind-speed estimation of the target elevation has been carried out by using the typical functions and their typical parameters, e.g., power and logarithmic functions because the available wind speed data is limited to the specific elevation, such as 2~3m, 10 m, and so on. In this study, the parameters of the vertical profile functions are estimated with optimal and analyzed the parameter ranges using the HeMOSU-1 platform wind data monitored at the eight different locations. The results show that the mean value of the exponent of the power function is 0.1, which is significantly lower than the typically recommended value, 0.14. The values of the exponent, the friction velocity, and the roughness parameters are in the ranges 0.0~0.3, 0~10 (m/s), and 0.0~1.0 (m), respectively. The parameter ranges differ from the typical ranges because the atmospheric stability condition is assumed as the neutral condition. To improve the estimation accuracy, the atmospheric condition should be considered, and a more general (non-linear) vertical profile functions should be introduced to fit the diverse profile patterns and parameters.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.5
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• pp.541-550
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• 2008

In this investigative study, the unit mass discharge for the major water quality parameters such as flowrate, SS, BOD, CODmn, CODcr, TN, TP from textile spinning/weaving industry nearby Nakdong river basin was estimated. To represent the respective industries, three companies from hundreds of textile spinning/weaving industries located in Nakdong river basin was carefully selected based on its manufacturing goods, flowrate and location for the estimation of unit mass discharge based on unit operation and process. There was a drastic decrease of unit mass discharge estimation between influents and effluents of water quality parameters, which represents the removal capacity of wastewater treatment plant. With the advent of new regulation on the imposed payment proportional to the total amount of pollutants discharge into the water body, the concept of cleaner production technology should be employed in the unit operation/process in wastewater treatment plant as well as textile manufacturing procedure to minimize the levy on the pollutants discharge. Unit mass discharge estimations of unit process (estimated in this study) in major water quality parameters (SS, BOD, COD, TN and TP) based on land were similar to those of composite process (estimated by National Institute of Environmental Research). But the unit mass discharge estimations of unit process in BOD and CODmn based on total sale were much higher than those of composite one while in SS, TN and TP similar to each other. For the detailed estimation of the imposed payment, unit mass estimation based on unit process should be further emphasized.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.772-778
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• 2012

In this paper, the states and the parameters in the dynamic system are simultaneously estimated by applying the UKF(Unscented Kalman Filter), which is widely used for estimating the state of non-linear systems. Estimating the parameter is very important in various fields, such as system control, modeling, analysis of performance, and prediction. Most of the dynamic systems which are dealt with in engineering have non-linearity as well as some noise. Therefore, the parameter estimation is difficult. This paper estimates the states and the parameters applying to the UKF, which is a non-linear filter and has strong noise. The augmented equation is used by including the addition of the parameter factors to the original state equation of the system. Moreover, it is simulated by applying to a 2-DOF(Degree of Freedom) dynamic system composed of the pendulum and the slide. The measurement noise of the dynamic equation is assumed to be a Gaussian distribution. As the simulation results show, the proposed parameter estimation performs better than the LSM(Least Square Method). Furthermore, the estimation errors and convergence time are within three percent and 0.1 second, respectively. Consequentially, the UKF is able to estimate the system states and the parameters for the system, despite having measurement data with noise.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.8
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• pp.619-624
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• 2013

To predict the probabilistic service life, statistical factors should be included to consider the uncertainty of parameters. Generally the probabilistic analysis is one of the common methods to account the uncertainty of parameters on the structural failure. In order to apply probabilistic analysis on the deterministic life analysis, it would be necessary to introduce Probability of Failure(PoF) and conduct risk assessment. In this work, we have studied probabilistic risk assessment of aircraft structures by using PoF approach. To achieve this goal, the Bayesian method was utilized to model PoF estimation since this method is known as the proper method to express the uncertainty of parameters. A series of proof tests were also conducted in order to verify the result of PoF estimation. The results from this efforts showed that the PoF estimation model can calculate quantitatively the value of PoF. Furthermore effectiveness of risk assessment approach for the aircraft structures was also demonstrated.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.148-154
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• 2014

This paper describes problems of the sensorless starting of horizontal axis washing machines with direct drive and suggests solution, which was experimentally verified. Horizontal axis washing machines have very difficult conditions for the drive starting, especially at full load. Inertia of the tub and water, torque from the laundry make load torque at starting higher than rated one and sometimes even higher than the maximum torque of the motor, which makes sensorless starting extremely challenging task. This paper suggests modified open-loop starting, where control system is closed shortly after beginning at low speed and rotates the drum until laundry restructuring. To ensure proper work of the sensorless algorithm at low speed additional measures for increasing of the estimation algorithm performance have been taken. These measures include special algorithm for the drive parameters estimation, which has been developed and verified by the experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.891-895
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• 2004

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm. The parameter estimation procedure is based on the steady state phase current versus slip and input power versus slip characteristics. The proposed estimation algorithm is of a nonlinear kind based on clonal selection in immune algorithm. The machine parameters are obtained as the solution of a minimization of least-squares cost function by immune algorithm. Simulation shows better results than the conventional approaches.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.1
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• pp.169-175
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• 2006

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1547-1554
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• 2011

As process parameters scale, interconnect width are reduced rapidly while the current flowing through interconnects does not decrease in a proportional manner. This effect increases current density in metal interconnects which may result in poor reliability. Since RMS(root-mean-square) current limits are used to evaluate self-heating and short-time stress failures caused by high-current pluses, RMS current estimation is very important to guarantee the reliability of semiconductor systems. Hence, it is critical to estimate the current limits through interconnects earlier in semiconductor design stages. The purpose of this paper is to propose a fast, yet accurate RMS current estimation technique that can offer a relatively precise estimate by using closed-form equations. The efficiency and accuracy of the proposed method have been verified through simulations using HSPICE for a vast range of interconnect parameters.