• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.10
• /
• pp.2067-2072
• /
• 2002

The closed-loop state and input observer is a pole-placement type observer and estimates unknown state and input variables simultaneously. Pole-placement type observers may have poor performances with respect to modeling error and sensing bias error. The effects of these ill-conditioning factors must be minimized for the robust performance in designing observers. In this paper, the steady-state performance of the closed-loop state and input observer is investigated quantitatively and is represented as the estimation error bounds. The performance indices are selected from these error bounds and are related to the robustness with respect to modeling errors and sensing bias. By considering both transient and steady-state performance, the main performance index is determined as the condition number of the eigenvector matrix based on $L_2$-norm.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
• /
• v.5 no.1
• /
• pp.46-52
• /
• 2002

A robust nonlinear observer, utilizing the sliding mode concept, is developed for the dynamic positioning of ships. The observer provides the estimates of linear velocities of the ship and bias from slowly varying environmental loads. It also filters out wave frequency motion to avoid wear of actuators and excessive fuel consumption. The main advantage of the proposed observer is in its robustness. Especially, the observer structure with a saturation function makes the proposed observer robust against neglected nonlinearties, disturbances and uncertainties. Since the mathematical model of DP ships is difficult to obtain and includes uncertainties and disturbances, it is very important for the observer to be robust. A nonlinear output feedback controller is derives based on the developed observer using the observer backstepping technique, and the global stability of the observer and control law is shown by Lyapunov stability theory.. A set of simulation was carried out to investigate the performance of the proposed observer for dynamic positioning of ships.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.04a
• /
• pp.21-26
• /
• 2003

The well-conditioned observer in a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic issues such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic issues such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_2$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic issues and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.04a
• /
• pp.39-44
• /
• 2003

It was shown that the robustness of deterministic observers with respect to modeling errors, measurement bias and round-off errors can be represented by a single performance index the condition number of the observer eigenvector matrix. In this paper, a robust observer for multi-output systems is designed using the left eigenstructure assignment, where the observer gain can not be determined uniquely with respect to the desired observer poles. Utilizing the eigenstructuer assignment for the robustness of the observer, the desired eigenvector matrix is selected to achieve the observer eigenvector matrix with the small condition number. The performance of the designed robust observer is evaluated in a spindle-drive simulation example where the load speed to be estimated based on the measured signals.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.11
• /
• pp.1621-1628
• /
• 2004

This paper proposes a design methodology for the robust observer using the eigenstructure assignment in multi-output systems so that the observer is less sensitive to the ill-conditioning factors such as unknown initial estimation error, modeling error and measurement bias in transient and steady-state observer performance. The robustness of the observer can be achieved by selecting the desired eigenvector matrix to have a small condition number that guarantees the small upper bound of the estimation error. So the left singular vectors of the unitary matrix spanned by space of the achievable eigenvectors are selected as a desired eigenvectors. Also, this paper proposes how to select the desired eigenvector based on the measure of observability and designs the observer with small gain. An example of a spindle drive system is simulated to validate the robustness to the ill-conditioning factors in the observer performance.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.3 no.1
• /
• pp.138-145
• /
• 2000

This paper presents a robust fault diagnosis and fault tolerant control method for the control systems in closed-loop affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the disturbance-decoupled state estimation using a 2-stage state observer for state, actuator bias and sensor bias. The estimated bias show the occurrence time, location and type of the faults directly. The estimated state is used for state feedback to achieve fault tolerant control against the faults. Simulation results show that the method has definite fault tolerant ability against actuator and sensor faults, moreover, the faults can be detected on-line, isolated and estimated simultaneously.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.5
• /
• pp.503-510
• /
• 2004

In this paper, the well-conditioned observer for a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic uncertainties such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic uncertainties such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_{2}$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic uncertainties. In stochastic viewpoints, the estimation variance represents the robustness to the stochastic uncertainties and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• Journal of Cardiovascular Imaging
• /
• v.31 no.3
• /
• pp.135-141
• /
• 2023

BACKGROUND: Global longitudinal strain (GLS) is an accurate and reproducible parameter of left ventricular (LV) systolic function which has shown meaningful prognostic value. Fast, user-friendly, and accurate tools are required for its widespread implementation. We aim to compare a novel web-based tool with two established algorithms for strain analysis and test its reproducibility. METHODS: Thirty echocardiographic datasets with focused LV acquisitions were analyzed using three different semi-automated endocardial GLS algorithms by two readers. Analyses were repeated by one reader for the purpose of intra-observer variability. CAAS Qardia (Pie Medical Imaging) was compared with 2DCPA and AutoLV (TomTec). RESULTS: Mean GLS values were -15.0 ± 3.5% from Qardia, -15.3 ± 4.0% from 2DCPA, and -15.2 ± 3.8% from AutoLV. Mean GLS between Qardia and 2DCPA were not statistically different (p = 0.359), with a bias of -0.3%, limits of agreement (LOA) of 3.7%, and an intraclass correlation coefficient (ICC) of 0.88. Mean GLS between Qardia and AutoLV were not statistically different (p = 0.637), with a bias of -0.2%, LOA of 3.4%, and an ICC of 0.89. The coefficient of variation (CV) for intra-observer variability was 4.4% for Qardia, 8.4% 2DCPA, and 7.7% AutoLV. The CV for inter-observer variability was 4.5%, 8.1%, and 8.0%, respectively. CONCLUSIONS: In echocardiographic datasets of good image quality analyzed at an independent core laboratory using a standardized annotation method, a novel web-based tool for GLS analysis showed consistent results when compared with two algorithms of an established platform. Moreover, inter- and intra-observer reproducibility results were excellent.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.1 s.232
• /
• pp.59-66
• /
• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• Korean Journal of Culture and Social Issue
• /
• v.20 no.2
• /
• pp.107-131
• /
• 2014

This driving simulation study examined relative differences in driving anger as the functions of drivers' gender and driving experiences, and actor-observer perspectives when they were exposed in two anger-provoking driving scenarios(cutting-in and sudden stop). The results showed the followings. First, neither drivers' gender nor driving experience, when they were considered independently of the driving situation types and actor-observer perspectives, yielded significant difference in driving anger. Second, actor-observer effect on driving anger was observed only in the cutting-in condition where other driver's intension was emphasized. Third, the female drivers of low driving experience tended to show the strongest tendency of actor-observer bias in the cutting-in condition. These results suggested that the levels of driving anger as the functions of drivers' gender and driving experience can be differed by types of driving situation as well as perspectives of drivers' interpreting the situations.