Individual lactate threshold estimation using recurrent neural networks.A standardization of the temporal axis to homogenize different length time-series.A modified stratified sampling for train-test set splitting of time-series.The system shows good individualization and generalization power.A non-invasive, cost efficient and accessible method to assess lactate threshold. Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires expensive equipment and the extraction of blood samples, which are inconvenient for frequent monitoring. Furthermore, most recreational runners do not have access to routine assessment of their physical fitness by the aforementioned equipment so they are not able to calculate the lactate threshold without resorting to an expensive and specialized center. Therefore, the main objective of this study is to create an intelligent system capable of estimating the lactate threshold of recreational athletes participating in endurance running sports.The solution here proposed is based on a machine learning system which models the lactate evolution using recurrent neural networks and includes the proposal of standardization of the temporal axis as well as a modification of the stratified sampling method. The results show that the proposed system accurately estimates the lactate threshold of 89.52 of the athletes and its correlation with the experimentally measured lactate threshold is very high (R=0.89). Moreover, its behaviour with the test dataset is as good as with the training set, meaning that the generalization power of the model is high.Therefore, in this study a machine learning based system is proposed as alternative to the traditional invasive lactate threshold measurement tests for recreational runners.
For instance, in @cite_40 a RNN was used to model dynamic non-linear systems of a gas turbine for simulation of its start-up operation. A NARX model without current time step data was used in this case and the final models were validated against other three experimental data sets. In this work, a single gas turbine was modeled using a database which covered all the start-up situations in which the gas turbines could work.
@cite_40
1764517434
Abstract In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation.