I. INTRODUCTIONE FFECTIVE transmission of high-frequency signals is usually achieved by the use of differential lines, that is, two signal wires/traces operated according to a differential signaling scheme. The advantages in terms of electromagnetic compatibility (EMC) properties are several. Namely, a differential line does not theoretically radiate and is ideally immune to external interfering fields, since unwanted common mode (CM) disturbance cancels out at the line terminals [1]. However, in actual differential pairs, the imbalance possibly introduced by uncertainty and tolerances in the manufacturing process [2] as well as by nonideal behavior of circuit components may seriously degrade EMC performance due to undesired conversion of the differential mode (DM) into CM, and vice versa. Namely, DMto-CM conversion is at the basis of unwanted radiated emissions (RE), whereas radiated susceptibility mainly originates from the conversion of the CM noise picked-up from external electromagnetic sources into DM disturbance at the ports of the drivers/receivers connected at the line ends.Due to the relevance of this problem, unbalanced transmission lines (TLs) and mode conversion have been extensively studied both from the theoretical and experimental viewpoints [3]- [13].
The design and integration of suspension parameters directly affects the riding quality of a rail vehicle. This study is intended to develop an approach to the optimization of suspension parameters of rail vehicles based on a virtual prototype Kriging model. To construct the virtual prototype Kriging model, a virtual prototype model of a rail vehicle and its suspension system was established based on a vertical model for its dynamics and using virtual prototype software. A virtual prototype Kriging model of a rail vehicle based on riding quality was also proposed, in which the training sample was obtained as different combinations of suspension parameters using the virtual prototype and dynamics simulations based on the design of experiments method. On this basis, an optimization model of the suspension parameters was established, in which the objective function was the Kriging model of the riding quality index. The optimized combination of suspension parameters was determined using the Multi-Island Genetic Algorithm. The dynamics simulation results before and after optimization for different rail profiles indicated that the riding quality was significantly improved, which demonstrated the universality and effectiveness of this approach.
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