• Journal of Chest Surgery
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• v.21 no.2
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• pp.223-230
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• 1988

Emax, end-systolic pressure-volume relationship, has been established as a new concept which can be representative of ventricular contractility itself since 1970s. Comparing to ejection fraction[EF], Emax is independent of preload and afterload. However Emax has not been proved precisely in non-thoracotomized condition because current methods have limitation in measuring ventricular chamber volume accurately in in viva state. The Dynamic Spatial Reconstructor[DSR], high speed computerized tomography, can measure ventricular chamber volume accurately throughout cardiac cycle in non-thoracotomized state. So Emax and EF of the left ventricle was tried to measure precisely in in vivo condition with DSR. Emax was compared to EF to estimate its ability to evaluate ventricular contractility. 5 mongrel dogs, weighing 15-16kg, were used for measuring Emax and EF of the left ventricle in 3 or 4 different loading conditions using DSR. Emax value in 5 dogs was from 2.62 to 10.49. Each dog has one Emax value regardless of loading conditions. However EF in 5 dogs varies depending on loading conditions. The conclusions are that Emax is useful in in viva state and EF varies depending on loading conditions. So Emax should be tried to use in clinical situation rather than EF because it is always representative of contractility itself regardless loading conditions in in viva state.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.58-66
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• 1996

An adaptive input-output linearization technique of an interior permanent magnet synchronous motor with a specified output dynamic performance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive technique where the stator resistance and the magnitude of flux linkage can be estimated with the current dynamic model and state observer. Using these estimated parameters, the linearizing control inputs are calculated. With these control inputs, the input-output linearization is performed and the load torque is estimated. The adaptation laws are derived by the Popov's hyperstability theory and the positivity concept. The robustness and the output dynamic performance of the proposed control scheme are verified through the computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.302-320
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• 2017

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

• Geotechnical Engineering
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• v.8 no.4
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• pp.67-80
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• 1992

In the analysis of dynamic problem, determination of mazimun shear modulus is essential for the estimation of shear stress at any strain level. Although many models for silica sands were presented, the direct accomodation of those models to crushable sand would be difficult because of crushability during torsion. In this research dynamic behaviour of tested sand is presented. The shear modulus of loose crushable sand shows similar results to silica sand. However, as the density of crushable sand increases the shear modulus decreases because of crushability by increasing surface contact area. And modulus number is expressed in terms of state parameter by Been and Jefferies (1965).

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.425-430
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• 1991

For satellite orbit determination, a satellite (K-3H) which is affected by the earth's gravitational field and the earth's atmospheric drag, the sun, and the moon is chosen as a dynamic model. The state vector include orbit parameters, uncertain parameters associated with perturbations and tracking stations. These perturbations include gravitational constant, atmospheric drag, and jonal harmonics due to the earth nonsphericity. Early orbit was obtained with given the predicted orbital parameter of the satellite. And orbit determination, which is applied to Extended Kalman Filter(EKF) for real time implementation , use the observation data which is given by satellite tracking radar system and then orbit estimation is accomplished. As a result, extended sequential estimation algorithm has a fast convergence and also indicate effectiveness for real time operation.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2320-2322
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• 2001

This paper describes the design of a robust adaptive neural-net(NN) observer for uncertain nonlinear dynamical system. The Lyapunov synthesis approach is used to guarantee a uniform ultimate boundedness property of the state estimation error, as well as of all other signals in the closed-loop system. Especially, for reducing the dynamic oder of the observer, we propose a new method in which no strictly positive real(SPR) condition is needed with on-line estimation of weights of the NNs. No a priori knowledge of an upper bounds on the uncertain terms is required. The theoretical results are illustrated through a simulation example.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.527-527
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• 2000

Wean modal parameter estiimation technique by developing a residual based system reconstruction and using the system matrix coordinate transformation. The modal parameters can be estimated from and residues of the system transfer functions expressed in modal coordinate basis, derived from the state space system matrices. However, for modal parameter estimation of mllltivariable and order structural systems over broad frequency bands, this non-iterative algorithm gives high accuracy in the natural fre and damping ratios. From vibration tests on cross-ply and angle-ply composite laminates, the natural frequencies and damping ratios can be estimated using the coordinates of the structural system reconstructed from the experimental frequency response. These results are compared with those of finite element analysis and single-degree-of-freedom curve-fitting..

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.4
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• pp.408-415
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• 2016

The vehicle attitude and sideslip is critical information to control the vehicle to prevent from unintended motion. Many of estimation strategy use bicycle model or IMU integration, but both of them have limits on application. The main purpose of this paper is development of vehicle orientation estimator which is robust to various vehicle state and road shape. The suggested estimator use 3-axis magnetometer, yaw rate sensor and lateral acceleration sensor to estimate three Euler angles of vehicle. The estimator is composed of two individual observers: First, comparing the known magnetic field and gravity with measured value, the TRIAD algorithm calculates optimal rotational matrix when vehicle is in static or quasi-static condition. Next, merging 3-axis magnetometer with inertial sensors, the extended Kalman filter is used to estimate vehicle orientation under dynamic condition. A validation through simulation tools, Carsim and Simulink, is performed and the results show the feasibility of the suggested estimation method.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.349-351
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• 2004

The classical dynamic backpropagation learning algorithm has the problems of learning speed and the determine of learning parameter. The Extend Kalman Filter(EKF) is used effectively for a state estimation method for a non linear dynamic system. This paper presents a learning algorithm using Dual Extended Kalman Filter(DEKF) for Fully Recurrent Neural Network(FRNN). This DEKF learning algorithm gives the minimum variance estimate of the weights and the hidden outputs. The proposed DEKF learning algorithm is applied to the system identification of a nonlinear SISO system and compared with dynamic backpropagation learning algorithm.

• Journal of Veterinary Clinics
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• v.16 no.2
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• pp.422-427
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• 1999

We are proposed for the computation of disease frequency measures and of their associated variances from data collected through prospective study of multiple dynamic cohort (herds) with a National Animal Health Monitoring System (NAHMS) in Gyeongnam. We can be estimated and calculated the annual incidence density for a group of herds and the 1-month risk of disease from the same within herd measure of monthly incidence density. We are proposed that the choice of measure to be estimated depend on the intended use of the information. From results in this study, Our study demonstrate that risk estimates are appropriates for producers and clinic veterinarian making decisions at the animal or herd level. Incidence density measures are appropriate for extrapolation to reference populations used for state and regional-level decision making.