High pressure common rail system is the state-of-the art technology for modern diesel engines to achieve energy conservation and emission reduction. The economy and emission performance of the diesel engines are influenced by the fuel injection stability of the high pressure common rail system. In this work, a high pressure common rail injector numerical model based on bond graph theory has been proposed. The comparisons between experimental results and numerical simulations show that the numerical model could reasonably predict the injection characteristics of the system. In order to reveal the essential rules and inherent characteristics of the fuel injection stability for high pressure common rail system, the rank variations of the state matrix at different injection pulse widths during fuel injection are obtained by means of a linear analysis. In addition, the distributions of eigenvalues for the state matrix in complex plane are investigated using Lyapunov method. The results show that the rank is influenced mainly by the movements of control valve and needle. Furthermore, the variation rule of the rank is independent of the injection pulse width before the needle is opened. The needle channel orifice of the injector plays a dominant role in transformation of the system from strong damping oscillation to underdamping oscillation. The opening and closing of control valve have significant effect on the stability of the system because its movement breaks the stability, and the movement of needle has remarkable effect on the oscillating characteristics of the system. High pressure common rail system is a complicated time-variant system with unstable pressure relief and strong oscillation injection.
Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.
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