• 전기의세계
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• v.22 no.2
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• pp.51-56
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• 1973

The comparative study of maximum likelihood estimation with Bayesian approach was made by statistical & computational methods in center of a priori information of static systems and the effect of a priori information on the accuracy of the estimatiion was also analyzed. Through the numerical computations of some examples by digital computer, we concluded that maximum likelihood method is better than Bayesian estimation except for almost certain a priori informations. The study may therefore contribute in identification problems of dynamical systems connected with a priori informations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.892-896
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• 2005

This paper presents method of depriving the relationship between static passive RFID tag and mobile robot In the field of tag-range. We use probabilistic sensor model of RFID reader by experiments. And we proposed estimation techniques by using direction of identification and relative-distance from the sensor model. Corresponding to distribution of identification, we can correct estimated tag position in relative coordinate. Simulation and Experimental Results show that the proposed method can provide good performance and thus be used fer mobile-robot localization.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.565-570
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Aerospace Engineering and Technology
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• v.2 no.1
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• pp.22-28
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• 2003

This paper presents a method for estimating static aerodynamic derivatives by analyzing data obtained from the flying quality evaluation test of a small canard aircraft. The aerodynamic derivatives extracted from maximum likelihood estimation method and from the proposed method in this paper are compared in the same polt. Reliable static aerodynamic derivatives were extracted from a limited number of the flight tests by the proposed method. The parameter data obtained from this method can be used as reference for the conventional parameter identification methods such as maximum likelihood estimation method.

• Journal of Korean Society of Steel Construction
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• v.15 no.6 s.67
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• pp.603-610
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• 2003

A damage identification technique using static displacement data is developed to asses s the structural integrity of bridge structures.As such, the relationship between static displacement and stiffness is derived, and the optimization technique utilized.Comparisons with numerical and experimental tests are performed to investigate the practical applicability of the proposed method.Various damage scenarios are considered by varying damage-width as well as damage-degree. The influence of noise in identifying the damage is also numerically investigated.Finally, the applicability and limitation of the proposed method are discussed.

• Journal of Astronomy and Space Sciences
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• v.28 no.4
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• pp.311-317
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• 2011

Momentum changing actuators like reaction wheels and control moment gyros are generally used for spacecraft attitude control. This type of actuators produces force and torque disturbances. These disturbances must be reduced since they degrade the quality of spacecraft attitude control. Major disturbances are mainly due to static and dynamic imbalances. This paper gives attention to the reduction of the static and dynamic imbalance. Force/torque measurement system is used to measure the disturbance of the test reaction wheel. An identification method for the location and magnitude of the imbalance is suggested, and the corrections of the imbalance are performed using balancing method. Through balancing, the static and dynamic imbalance is remarkably reduced.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.73-80
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• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.89-96
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• 2023

The Wiener-type nonlinear model where a static nonlinear block follows a dynamic linear block is widely used to describe the dynamics of chemical processes. A long process excitation step is typically needed to identify this Wiener-type nonlinear model with two blocks. In order to cope with this disadvantage, an identification method for the Wiener-type nonlinear model that uses only a single-step response is proposed here. The proposed method estimates the response of the dynamic linear sub-block from the initial part of the step response, and then the static nonlinear sub-block is identified. Because the only single-step response is used to identify the Wiener-type nonlinear model, there is great benefit in time and cost for obtaining process response. The performance of the proposed identification method with the single-step response is verified through a representative Wiener-type nonlinear process, a pH titration process, and a liquid level system.

• Steel and Composite Structures
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• v.9 no.5
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• pp.445-455
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• 2009

This study proposes a system identification technique for a fiber-reinforced polymer deck with neural networks. Neural networks are trained for system identification and the identified structure gives training data in return. This process is repeated until the identified parameters converge. Hence, the proposed algorithm is called an iterative neural network scheme. The proposed algorithm also relies on recent developments in the experimental design of the response surface method. The proposed strategy is verified with known systems and applied to a fiber-reinforced polymer bridge deck with experimental data.