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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...
Thanks to the advances of data acquisition techniques, we can acquire ventricular blood flow data with very high quality. This extremely complex spatiotemporal data calls for novel visualization and analysis tools. In particular, the new tools need to assist domain experts in quick identification of critical patterns. In this paper, we present a method using topo-logical data analysis tools with simulated...
Testing is an important aspect in effectiveness verification of the composability of simulation model. This paper introduces a context-driven testing approach that supports to confirm and improve the composability of model in composable simulation research. Context-driven testing is a feasible approach that specifies model test framework and algorithms of constraint modeling for test on three hierarchical...
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