• Geomechanics and Engineering
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• v.34 no.2
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• pp.187-195
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• 2023

Intensive rainfall during the summer season in Korea has triggered numerous devastating landslides outside of downtown in mountainous areas. The 2D slope stability analysis that is generally used for cut slopes and embankments is inadequate to model slope failure in mountainous areas. This paper presents a new 3D slope stability formulation using the global sliding vector in the limit equilibrium method, and it uses an ellipsoidal slip surface for static and quasi-static analyses. The slip surface's flexibility of the ellipsoid shape gives a lower FS than the spherical failure shape in the Fellenius, Bishop, and Janbu's simplified methods. The increasing sub-columns of each column tend to increase the FS and converge to a steady value. The symmetrical geometric conditions of the convex turning corners do not indicate symmetrical failure of the surface in 3D analysis. Pseudo-static analysis shows that the horizontal seismic force decreases the FS and increases the mass volume at the critical failure state. The stability index takes the FS and corresponding sliding mass into consideration to assess the potential risk of slope failure in complex mountainous terrain. It is a valuable parameter for selecting a vulnerable area and evaluating the overall risk of slope failure.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.673-676
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• 1993

The field-oriented control of induction motor without speed sensor has been widely studied. This paper proposes the new design method of adaptive sliding observer for induction motor, which include the rotor speed identification together with the rotor flux estimation. The proposed adaptive observer has advantage of their global stability which is developed on some assumption. It is easy to analyse because the identifier is seperated from the controller. Finally, the chattering which is caused by switching is reduced by new control scheme, and the validaity of the adaptive algorithm is verified by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.