Incucomm, Inc.

Randal Allen

Eric Haney

John P Volpi

Steven D Roemerman

Matthew Bowers

Shane Poage

James Robinson

Christopher Niblo

Mark James Volpi

Brice Brosig

A methodology for correlated histogram clustering for machine learning which does not require a priori knowledge of cluster numbers, which extends beyond bimodal scenarios to multimodal scenarios, and does not need iterative optimization methods nor require powerful data processing.

Methods for correlated histogram clustering for machine learning

Systems and methods for adaptively optimizing a performance function for operating a system. A performance function for a system is adaptively optimized, and utilized for the operation of the system, by selecting a first value for a first independent variable that determines an operating state of the system described by a performance function; establishing a first range of values for the first independent variable; selecting a first number of values in the first range of values to test the first independent variable; selecting first random values within the first range of values for the first independent variable based on the first number of values; evaluating the performance function at the first random values; optimizing the performance function by selecting the first value of the first independent variable from the first random values that provides a first extremum value for the performance function.

System and method for adaptive optimization

Systems and methods for the automatic routing of data packets in dynamic networks, wherein at least a portion of a route for a data packet is modeled as a fluid dynamics potential flow characterized by an irrotational velocity field. Each data packet is the analogue of a flow particle, an originating node of the data packet is the analogue of a source, and a destination node of the data packet is the analogue of a sink. Each of one or more nodes intermediate to the originating node and the destination node for a data packet are defined as a stream function (Ψ) which adheres to the definition of irrotational and incompressible potential flow that independently represents a flow phenomenon that can influence the route of the data packet. A route for a data packet is calculated based on its current location and the aggregate stream function comprising the sum of each of the flow phenomena acting on the data packet.

System and method for the automatic routing of information in dynamic networks

A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.

Adaptive distributed analytics system

An aggregation layer subsystem, and method of operation thereof, for use with an architect subsystem and a plurality of edge processing devices in a distributed analytics system, wherein each edge processing device is adapted to monitor and control the operation of at least one monitored system according to a first analytic model, the aggregation layer subsystem comprising: a processor and memory, the memory containing instructions which, when executed by the processor, enables the aggregation layer subsystem to: receive a second analytic model from the architect subsystem, the second analytic model based on characteristics of at least one monitored system associated with at least one of the plurality of edge processing devices; receive monitored system information from each of the plurality of edge processing devices; and, provide control signals to the at least one monitored system, via one of the edge processing devices, according to the second analytic model in response to the monitored system information.

Distributed analytics system used to control the operation of at least one monitored system; the system includes an architect subsystem and an edge subsystem, wherein the edge subsystem comprises at least one edge processing device associated with at least one monitored system. The architect subsystem deploys at least one analytic model to an edge processing device based on characteristics of a monitored system associated with the edge processing device, the analytic model to be used by the edge processing device to provide control signals to a monitored system; and, receives information related to the monitored system from the edge processing device, the information utilized by the architect subsystem to modify the analytic model deployed to the at least one edge processing device to improve system performance of the monitored system. An edge processing device receives an analytic model from the architect subsystem; provides control signals to the monitored system according to the analytic model; and, sends information related to the monitored system to the architect subsystem, the information to be used by the architect subsystem to modify the analytic model to improve system performance of the monitored system.

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Orlando, FL, United States of America

Randal Allen