Abstract: Apparatus, computer-readable medium, and method for optimal delivery of one or more satellite effects via reverse time heat equation isomorphism including constructing a generalized formulation of the heat equation corresponding to a commander's intent for delivery of the one or more satellite effects, performing a separation of variables to decouple a plurality of variables into a plurality of corresponding separated equations, solving the plurality of separated equations to produce candidate solutions, applying boundary conditions to each candidate solution, filtering the candidate solutions to mitigate ill-posedness of solving the inverse of the heat equation, assessing formal differences between the candidate solutions and the inverse of the heat equation, and generating an optimized schedule of the one or more satellite effects based on the assessment of the formal differences of the candidate solutions.

Abstract: Systems, devices, methods, and computer-readable media for normalized (threat, effect) pair algorithm generation. A method can include, for a defined scenario, identifying (threat, effect) pairs and for each (threat, effect) pair of the identified (threat, effect) pairs categorizing metrics of an algorithm, the algorithm indicating a probability of mitigating damage, by the effect, caused by the threat and generating a normalized algorithm for the identified (threat, effect) pair based on the metrics, the normalized algorithm operating based on input parameters of same units as other normalized algorithms.

Abstract: A computing machine stores representations of a plurality of assets. The computing machine receives a representation of a course of action (COA), the COA making use of all or a subset of the plurality of assets, a representation of movement of the all or the subset of the plurality of assets across time, and a goal. The computing machine identifies one or more mission phases and/or activities in the COA and one or more assets or asset pairings for use in each mission phase and/or activity. The computing machine computes a set of values for a given mission phase and/or activity. The computing machine logs the computed set of values. The computing machine stores metrics representing the computed set of values and the one or more mission phases and/or activities in the COA. The metrics are used to verify the results of the computations.

Abstract: A system and method improves the probability of correctly detecting an object from a collection of source data and reduces the processing load. A plurality of algorithms for a given data type are selected and ordered based on a cumulative trained probability of correctness (Pc) that each of the algorithms, which are processed in a chain and conditioned upon the result of the preceding algorithms, produce a correct result and a processing. The algorithms cull the source data to pass forward a reduced subset of source data in which the conditional probability of detecting the object is higher than the a priori probability of the algorithm detecting that same object. The Pc and its confidence interval is suitably computed and displayed for each algorithm and the chain and the final object detection.

Abstract: Generally discussed herein are systems, devices, and methods for mitigating damage caused by a hazard. A method can include identifying at least two effects that, with some probability, at least partially mitigate the hazard, identifying one or more vulnerabilities of the hazard that are the target for an effect of the identified effects, for each hazard, vulnerability pair, identifying a respective hazard model that simulates a state of the hazard in response to the effect, identifying effect models that simulate the respective effects, normalizing each of the identified effect models to a common model and determining a confidence level for each parameter of each normalized model, and simulating combinations of effects by combining normalized models and recording their combined effect on the hazard and a corresponding combined confidence level for the normalized models.

Abstract: A system and method improves the probability of correctly detecting an object from a collection of source data and reduces the processing load. A plurality of algorithms for a given data type are selected and ordered based on a cumulative trained probability of correctness (Pc) that each of the algorithms, which are processed in a chain and conditioned upon the result of the preceding algorithms, produce a correct result and a processing. The algorithms cull the source data to pass forward a reduced subset of source data in which the conditional probability of detecting the object is higher than the a priori probability of the algorithm detecting that same object. The Pc and its confidence interval is suitably computed and displayed for each algorithm and the chain and the final object detection.