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Long pulse modulator systems like the electronpositron compact linear collider (CLIC) with a pulse length of 140 µs require a bouncer for voltage drop compensation. In this paper, an active bouncer system, consisting of four bouncer modules, in series to the main capacitor bank, is investigated. The system's transfer function including the matrix pulse transformer is analysed and the output voltage ripple induced by the bouncer system is designed to be smaller than 5 ppm due to demanding repeatability requirement of CLIC. Based on a loss analysis is conducted resulting in a total bouncer system volume of 52 dm 3 and a reduction in the global efficiency (grid to klystron) of the modulator system of only 0.45 %.

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Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge is required for the application. At the same time, hyperparameters have a particularly large influence on the quality of the model. These parameters influence the architecture and the training process of the machine learning algorithm. This paper deals with the comparison of different hyperparameter optimization methods for the design of a roll angle estimator based on an artificial neural network. The comparison is drawn based on a pre-generated simulation data set created with ISO standard driving maneuvers. Four different optimization methods are used for the comparison. Random Search and Hyperband are two similar methods based purely on randomness, whereas Bayesian Optimization and the genetic algorithm are knowledge-based methods, i.e., they process information from previous iterations. The objective function for all optimization methods consists of the root mean square error of the training process and the reference data generated in the simulation. To guarantee a meaningful result, k-fold cross-validation is integrated for the training process. Finally, all methods are applied to the predefined parameter space. It is shown that the knowledge-based methods lead to better results. In particular, the Genetic Algorithm leads to promising solutions in this application.

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