Bong-Yeol Choi

Fast Preisach modeling method for shape memory alloy actuators using major hysteresis loops

The control accuracy of smart actuators, such as a shape memory alloy (SMA) or piezoceramic actuator, is limited due to their inherent hysteresis nonlinearities with a local memory, resulting from the influence of a previous input on subsequent behavior. In addition, the existence of minor loops in the major loop because of a local memory also makes the mathematical modeling and design of a controller difficult for SMA actuators. Therefore, to enhance the controllability of a smart actuator, the Preisach hysteresis model has emerged as an appropriate behavioral model, yet the modeling is difficult and the model equation complex. Accordingly, to resolve these difficulties, the current paper proposes a simple method based on applying the proportional relationship between the major loop and the FOD curves of an SMA actuator to the Preisach model. As such, using only data for the major hysteresis loop, the proposed method enables the FOD curves to be easily approximated and the output length rapidly computed. The efficacy of the proposed Preisach modeling method is confirmed based on comparative experiments with the classical Preisach model.

Image thresholding using standard deviation

Threshold selection using the within-class variance in Otsu’s method is generally moderate, yet inappropriate for expressing class statistical distributions. Otsu uses a variance to represent the dispersion of each class based on the distance square from the mean to any data. However, since the optimal threshold is biased toward the larger variance among two class variances, variances cannot be used to denote the real class statistical distributions. Therefore, to express more accurate class statistical distributions, this paper proposes the within-class standard deviation as a criterion for threshold selection, and the optimal threshold is then determined by minimizing the within-class standard deviation. Experimental results confirm that the proposed method produced a better performance than existing algorithms.

On-Line Directional Algebraic Reconstruction Technique for Electrical Capacitance Tomography

In general, image reconstruction algorithms for electrical capacitance tomography (ECT) can be classified into iterative and noniterative algorithms. Iterative algorithms produce better quality images however they can only be used off-line due to intensive computational burden. In the context of computational burden, in this paper we propose a fast noniterative image reconstruction algorithm called on-line directional algebraic reconstruction technique (OLDART) which like its predecessor, directional algebraic reconstruction technique (DART), produces the same quality image but reduces the reconstruction time. The reconstruction procedure of the proposed algorithm comprises of two steps. In the first step, a modified weighting matrix is generated off-line, and in the second step, the matrix is used for on-line image reconstruction in the same manner as the sensitivity matrix in the linear back-projection algorithm is used. In order to assess the reconstruction performance, extensive simulation results are provided for the proposed approach.