• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.697-702
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• 2004

In this paper, we proposed that the problem of controlling induction motor with magnetic saturation, is studied from an input-output feedback linearization with adaptive algorithm. is considered. An adaptive input-output feedback linearizing controller is considered under the assumption of known motor parameters and unknown load torque. In order to achieve the speed regulation with the consideration of improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. Simulation results are provided for illustration.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.5
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• pp.380-385
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• 2020

Over the past decade, small robots have been of particular interest in the engineering field. Among the various types of small robots, biomimetic robots, which mimic animals and insects, have been developed for special activities in areas where humans cannot physically access. The optimal motion of a walking robot can be determined by the characteristics of the traversed surface (e.g., roughness, curvature, slope, materials, etc.). This study proposes three types of piezoelectric structures using different driving mechanisms, depending on the application range of the small walking robots. Dynamic modeling using computer-aided engineering optimized the shape of the robot to maximize its moving characteristics, and the results were also verified through its fabrication and experimentation. Three types of robots, named by their actuator shapes as I, π, & T-shape, were proposed regarding application for small scale ambulatory robots to different terrain conditions. Among these, the T-shaped robots were shown to have a wide range of speeds (from 2 mm/s up to 255 mm/s) and good carrying capacity (up to 10 g at 50 mm/s) through driving experiments. Based on this study, we proposed possible application areas for the three types of walking robot actuators.

• Transactions of Materials Processing
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• v.24 no.6
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• pp.395-399
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• 2015

A microstructure-based finite element simulation was conducted to predict the plastic strain ratio (R-value) of a dual-phase (DP) steel. The representative volume elements (RVEs) concept was adopted for the image-based FE modeling and a 3D model was constructed using sequential 2D images. Each phase was considered with the von-Mises yield criterion and the Swift model. The Swift parameters were defined by the empirical equations based on the chemical composition. The developed model was applied to analyze the effect of residual stress on the R-value and stress distribution. In order to consider the residual stress development after cold rolling, 10 % compression was applied in the thickness direction and unloaded before the tensile stress was applied in the rolling direction. The results showed a reasonable prediction for the R-value evolution: a sharp increase at small strains was well described and a transition followed in the downward direction. The R-value evolution was analyzed using the stress distribution change on the π-plane

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.