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This paper presents a transfer learning-based approach for bearing fault diagnosis, where the transfer strategy is proposed to improve diagnostic performance of the bearings under various operating conditions. The main idea of transfer learning is to utilize selective auxiliary data to assist target data classification, where a weight adjustment between them is involved in the TrAdaBoost algorithm...
One of the major obstacles in computational modeling of a biological system is to determine a large number of parameters in the mathematical equations representing biological properties of the system. To tackle this problem, we have developed a global optimization method, called Discrete Selection Levenberg-Marquardt (DSLM), for parameter estimation. For fast computational convergence, DSLM suggests...
This paper introduces two newly developed models of capacitive silicon bulk acoustic resonators (SiBARs). The first model is analytical and is obtained from an approximate solution of the linear elastodynamics equations for the SiBAR geometry. The second is numerical and is based on finite-element, multi-physics simulation of both acoustic wave propagation in the resonator and electromechanical transduction...
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