• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.1
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• pp.13-20
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• 2020

Accurate parameters based on equivalent circuit are required for high-performance field-oriented control in a three-phase induction motor. In a normal case, stator resistance can be accurately measured using a measuring equipment. Except for stator resistance, all machine parameters on the equivalent circuit should be estimated with particular algorithms. In the viewpoint of traditional regions, the parameters of an induction motor can be identified through the no-load and standstill test. This study proposes an identification method that uses the d-axis model of the induction motor in a stationary frame with the predefined information on stator resistance. Mutual inductance is estimated on the rotational dq coordination similar to that in the traditional no-load experiment test. The leakage inductance and rotor resistance can be estimated simply by applying different voltages and frequencies in the d-axis model of the induction motor. The proposed method is verified through simulation and experimental results.

• Smart Structures and Systems
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• v.15 no.1
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.365-372
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• 2019

We propose a method to estimate unknown parameters(e.g., target amplitude and clutter parameters) in the generalized likelihood ratio test(GLRT) using maximum likelihood estimation and the Newton-Raphson method. When detecting targets in a clutter environ- ment, it is important to establish a modular model of clutter similar to the actual environment. These correlated clutter models can be generated using spherically invariant random vectors. We obtain the GLRT of the generated clutter model and check its detection probability using estimated parameters.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.17-24
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• 2019

In this paper, we analyzed the effect of power system load model on the short-term voltage stability analysis results. First, we introduced common load models. We also confirmed that some load models can not represent actual system phenomena even if the model parameters are optimized. Also, we studied about the influences of load parameters and regional characteristics of load model on the sort-term voltage stability of KEPCO power system considering the contingency. The results showed that the importance of selecting a load model was confirmed again. And we recognized about it can be understood that it should reflect the load characteristics of the area near the assumed contingency more accurately.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4127-4133
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• 2024

Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.128-133
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• 2002

• Proceedings of the Korean Nuclear Society Conference
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• 2002.10a
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• pp.355.2-355
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• 2002

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.636-637
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.638-643
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• 2001