Abstract: There are provided methods and devices for use with industrial control systems. For example, there is provided a method that can include acquiring data from a sensor associated with an industrial machine. The method can include determining, while acquiring the data, whether at least one condition is satisfied in the data. Furthermore, the method can include logging the data as a function of time and logging information associated with the at least one condition as a function of time, in response to the at least one condition being satisfied. The method can also querying the logged information, subsequent to logging the data, to identify subsets of the data for which the at least one condition is satisfied.

Abstract: Proposed is a ML-based, predictive, digital underwriting system and corresponding method providing an automated parameter-driven predictive underwriting process based on measured probability values associated with individuals of a cohort or portfolio, the individuals being exposed to a probability of occurrence of one or more predefined medical and/or health and/or life events having the probability value with a predefined severity within a future measuring time-window.

Abstract: The present invention concerns a method of sampling a test signal. The method comprises: acquiring training signals sampled at a plurality of sampling locations; running an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling the test signal at the sampling locations represented by the n indices.

Abstract: The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling (25) the test signal at the sampling locations represented by the n indices.