Neural Network and Training Strategy Design for Train Drivers’ Vibration Dose Simulation

Abstract

Vibration can cause professional illnesses in train drivers, giving also rise to lawsuits to the employer. A possible cause may be the lack of systematic vibration estimation processes, due to operational complexities, subjectivities involved and the cost of dedicated tests. Estimation quality may be improved by using a driver seat model along with cabin floor vibration data acquired during the train dynamic approval tests. However, due to the nonlinearities present, analytical models frequently show inaccurate results. This work deals with the design of an appropriate neural network for predicting the seat-driver interface vibration, based on selected and processed cabin floor acceleration data obtained during the dynamic approval tests. Network type, input signals set and signal conditioning have considerable impact on the simulation accuracy. Results show good correlation between simulated and experimental data, even better between simulated and measured standard vibration dose indicators, being RMS errors between 3.9 % and 9.4 % and peak factor errors between 0.8 % and 9.6 %.

Behavioral Feature Description Method Based on the Vector Module Ratio and Vector Angle of Human Body Structure

“…We choose BP neural network [29]- [31] as the classification model, and use Matlab to test the effect of the feature description method proposed in this paper on the self built data set described above. Table 5 shows the parameters of the neural network we use.…”

An Ensemble Learning Model for Short-Term Passenger Flow Prediction

“…e results show that the predicted results can accurately reflect the time change rule of passenger flow in and out of Beijing subway station. Because these parametric models assume linear relationships between variables with time delay, it is difficult to capture nonlinear relationships between variables, so the use of traditional parametric methods is limited [7,8].…”

Product-harm crisis intelligent warning system design based on fine-grained sentiment analysis of automobile complaints

“…In 2013, Habibi et al proposed weighted calculations of the influence probability transition matrix transmitted by adjacent words using various graph centrality indicators, such as degree centrality and proximity centrality in a word graph, to improve keyword extraction [9]. Munoz-Guijosa proposed a neural network training strategy when simulating train drivers' vibration [10]. Siu mined topic information and keywords by training a hidden Markov model and achieved ideal results on a test corpus [11].…”