Abstract: A system for training a machine learning model using a batch based active learning approach. The system includes an information source and an electronic processor. The electronic processor is configured to receive a machine learning model to train, an unlabeled training data set, a labeled training data set, and an identifier of the information source. The electronic processor is also configured to select a batch of training examples from the unlabeled training data set and send, to the information source, a request for, for each training example included in the batch, a label for the training example. The electronic processor is further configured to, for each training example included in the batch, receive a label, associate the training example with the label, and add the training example to the labeled training data set. The electronic processor is also configured to train the machine learning model using the labeled training data.

Abstract: A system for training a deep learning system to detect engine knock with accuracy associated with high fidelity knock detection sensors despite using data from a low fidelity knock detection sensor. The system includes an engine, a high fidelity knock detection sensor, a low fidelity knock detection sensor, and an electronic processor. The electronic processor is configured to receive first data from the high fidelity knock detection sensor. The electronic processor is also configured to receive second data from the low fidelity knock detection sensor. The electronic processor is further configured to map the first data to the second data, train the deep learning system, using training data including the mapped data, to determine a predicted peak pressure using data from the low fidelity knock detection sensor, receive third data from the low fidelity knock detection sensor, and using the third data, determine the predicted peak pressure.

Abstract: A system for training a machine learning model using a batch based active learning approach. The system includes an information source and an electronic processor. The electronic processor is configured to receive a machine learning model to train, an unlabeled training data set, a labeled training data set, and an identifier of the information source. The electronic processor is also configured to select a batch of training examples from the unlabeled training data set and send, to the information source, a request for, for each training example included in the batch, a label for the training example. The electronic processor is further configured to, for each training example included in the batch, receive a label, associate the training example with the label, and add the training example to the labeled training data set. The electronic processor is also configured to train the machine learning model using the labeled training data.

Abstract: A system for training a deep learning system to detect engine knock with accuracy associated with high fidelity knock detection sensors despite using data from a low fidelity knock detection sensor. The system includes an engine, a high fidelity knock detection sensor, a low fidelity knock detection sensor, and an electronic processor. The electronic processor is configured to receive first data from the high fidelity knock detection sensor. The electronic processor is also configured to receive second data from the low fidelity knock detection sensor. The electronic processor is further configured to map the first data to the second data, train the deep learning system, using training data including the mapped data, to determine a predicted peak pressure using data from the low fidelity knock detection sensor, receive third data from the low fidelity knock detection sensor, and using the third data, determine the predicted peak pressure.