Abstract: A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.

Abstract: A method for generating synthetic time series for augmenting a training data set of training time series used for training a machine learning model.

Abstract: A method for synchronizing time series of sensor values relating to a manufacturing process. The method includes: detecting at least two time series of sensor values by the same sensor during each of at least two executions of the manufacturing process; ascertaining, for each of the at least two time series of sensor values, deviations of different points of the corresponding time series from corresponding points of a reference time series and shifting the corresponding time series by a value ascertained based on the ascertained deviations, in order to synchronize the time series; and providing the synchronized time series.

Abstract: A method for providing wafer test data of at least one wafer with semiconductor chips, in particular for providing a training data set for a machine learning algorithm for anomaly detection, comprising receiving a set of measured variables from the semiconductor chips of the wafer, defining sub-areas on the wafer, each of which comprises a plurality of semiconductor chips of the wafer for which measured variables have been received, and outputting a reduced set of measured variables compared to the received set of measured variables, which comprises only the measured variables of subsets of the semiconductor chips of the respective sub-areas.

Abstract: A computer-implemented method for optimizing a detection threshold of a prediction model used to determine an anomaly of a component is disclosed. The detection threshold indicates the criterion above which the prediction model classifies a component as anomalous.

Abstract: A method and system is disclosed for creating a machine learning model that is reconfigurable. A fixed parameter model is created to include fixed feature values obtained during a training process for the machine learning model. The fixed parameter model may include a fixed base classifier used by the machine learning model to classify objects detected by an ultra-sonic system within a vicinity of a vehicle. A configurable parameter model may be created to include feature values that are different from the fixed feature values, the configurable parameter model including a modified base classifier. A vehicle controller may receive and update the fixed parameter model with the configurable parameter model. The machine learning model may be updated to use the configurable parameter model to classify the objects detected by the ultra-sonic system.

Abstract: A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.

Abstract: A system and method is disclosed for classifying one or more objects within a vicinity of a vehicle. Ultra-sonic data may be received from a plurality of ultra-sonic sensors and may comprise echo signals indicating one or more objects that are proximally located within a vicinity of a vehicle. One or more features may be calculated from the ultra-sonic data using one or more signal processing algorithms unique to each of the plurality of ultra-sonic sensors. The one more features may be combined using a second-level signal processing algorithm to determine geometric relations for the one or more objects. The one or more features may then be statistically aggregated at an object level. The one or more objects may then be classified using a machine learning algorithm that compares an input of each of the one or more features to a trained classifier.