In this paper, the knock phenomenon is studied and characterized by time-frequency analysis and a new definition that is capable of differentiating normal combustion from autoignition of the end gas, is proposed. The new definition permits detecting low-knocking cycles, and consequently, more knocking information is available for updating knock models or for improving knock control strategies. The new definition of knock is implemented online in a four stroke SI engine and its performance is illustrated by using a classical knock control strategy. Results obtained under different operating conditions demonstrate that the improved knock definition can substantially reduce the dispersion of Spark advance angle control and reach higher mean values, obtaining then, higher combustion efficiencies and reducing engine vibration.
This paper deals with collision and hazard detection for motorcycles via inertial measurements. For this kind of vehicles, the most difficult challenge is to distinguish road's anomalies from real hazards. This is usually done by setting absolute thresholds on the accelerometer measurements. These thresholds are heuristically tuned from expensive crash tests. This empirical method is expensive and not intuitive when the number of signals to deal with grows. We propose a method based on self-organized neural networks that can deal with a large number of inputs from different types of sensors. The method uses accelerometer and gyro measurements. The proposed approach is capable of recognizing dangerous conditions although no crash test is needed for training. The method is tested in a simulation environment; the comparison with a benchmark method shows the advantages of the proposed approach.
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