• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.333-338
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• 1993

A feedback linearization controller for EMS system is implemented using DSP. In this paper, we show that given EMS system is input-state linearizable and satisfies some robustness condition. Also we derive feedback linearization controller for given system. Finally, some experiments are performed to demonstrate the performance of the proposed controller-especially, comparing this with the classical state feedback controller using linear perturbation.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.45-46
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• 2005

In this paper, a passive telemetry sensor system using Unscented Kalman Filter(UKF) is proposed. Specially, to show the effective tracking performance of the UKF, we compared with the tracking performance of Recursive Least Square Estimation (RLSE) using linearization.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.973-975
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• 1998

For a nonlinear feedback linearization-full order observer/sliding mode controller (NFL-FOO/SMC), the separation principle is derived, and the closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.985-987
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-full order observer-based sliding mode controller (NFL-FOO-based SMC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.967-969
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• 1998

In this paper, the proposed controller is obtained by combining the nonlinear feedback linearization-sliding mode controller (NFL-SMC) with the full order observer (FOO) and eliminates the need to measure all the state variables in the conventional NFL-SMC.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.976-978
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• 1998

This paper presents the stability proof of a nonlinear feedback linearization-reduced order observer/sliding mode controller (NFL-ROO/SMC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.979-981
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-observer/sliding mode model following controller (NFL-O/SMMFC). The separation principle is derived, and the closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.988-990
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• 1998

This paper presents the stability proof of a nonlinear feedback linearization-reduced order observer-based sliding mode controller (NFL-ROO-based SMC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.991-993
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-observer-based sliding mode model following controller (NFL-O-based SMMFC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Journal of Yeungnam Medical Science
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• v.16 no.2
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• pp.237-243
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• 1999

We conducted this study to find the optimal correction factor(${\alpha}$) of Lassen's linearization algorithm which has been applied for correction of flow-limited uptake at a high flow range in $^{99m}Tc$ d,l-hexamethylpropy leneamine oxime(HMPAO) and $^{99m}Tc$ ethyl cysteinate dimer(ECD). Ten patients with chronic cerebral infarction were involved in this study. We obtained the corrected $^{99m}Tc$ HMPAO and $^{99m}Tc$-ECD brain SPECT(single photon emission computed tomography) using the algorithm with ${\alpha}$ values that varied from 0.1 to 10 and compared the results with regional cerebral blood flow determined by positron emission tomography (PET-rCBF). The multi-modal volume registration by maximization of mutual information was used for matching between PET-rCBF and SPECT images. The highest correlation coefficient between $^{99m}Tc$-HMPAO and $^{99m}Tc$-ECD brain uptake and PET-rCBF was revealed at ${\alpha}$ 1.4 and 2.1, respectively. We concluded that the ${\alpha}$ values of Lassen's linearization algorithm for $^{99m}Tc$-HMPAO and $^{99m}Tc$-ECD brain SPECT images were 1.4 and 2.1, respectively to indicate cerebral blood flow with comparison of PET-rCBF.