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In this paper, a novel algorithm (ILC-EMD) that integrates iterative learning control (ILC) with empirical mode decomposition (EMD) is proposed to improve learning process. To explain the divergence behavior under the conventional ILC, the EMD is utilized to decompose the tracking error signal into 11 intrinsic mode functions (IMFs). By observing the root mean square and the correlation values of the IMFs during iterations, the first IMF is determined to be the undesired signal which could not be reduced by learning process. Furthermore, the command containing the first IMF could further excite the machine tool due to the resonance effects and cause the amplification of the error signal. The ILC-EMD can filter out the undesired signal and prevent the amplification effect. Experimental results on tracking the butterfly and dragon nonuniform rational B-spline curves validate the effectiveness of the ILC-EMD algorithm. [ABSTRACT FROM PUBLISHER]

vA generalized model that integrates the Navier- Stokes equation and coupling-of-modes (COM) model for biosensing SAW devices is developed in this paper. The SAW device is separated into three regions: interdigital transducer (IDT), substrate (delay line), and sensing regions. To evaluate the effects of metal thickness, mass loading caused by bioreaction, and different viscous fluid loading, the sensing region is further divided into three layers: piezoelectric substrate, metal layer, and fluid layer. In contrast to the conventional study, which is focused on the change of phase velocity, this model can evaluate the insertion loss and phase shifts under different sensing conditions. It can be shown that the integration of the COM model can provide guidelines for designing the bio-sensing device such as choosing the proper number of IDT, the width of the overlap, and the thickness of the metal layer. Furthermore, the generalized model can be utilized to evaluate the optimal thickness of the metal layer to achieve the maximum sensitivity. [ABSTRACT FROM PUBLISHER]