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The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations. [ABSTRACT FROM AUTHOR]

The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations.

This paper is primarily focused on the problem of maximizing glycemia prediction accuracy for the type 1 diabetic subjects, particularly if the linear empirical models are used. The two-input Box-Jenkins model has been preferred since its structure reflects the actual differences in the dynamics of the insulin administration and the carbohydrates intake inputs. Additionally, the basal state of the subject was integrated directly into to model yielding the ability to estimate the basal glycemia within the identification procedure. Since it is common to experience issues with the physiology compliance of empirical models, it was proposed to perform the multi-stepahead predictive identification of the zero-pole-gain representation with applied constraints. The effect of the excitation signals on the quality of estimated models has also been marginally analyzed, concluding that applying the variable insulin-carbohydrates ratio between individual insulin boluses is a suitable strategy. [ABSTRACT FROM AUTHOR]