Abstract: A system and method for constructing an initial mathematical system representation with a combination of terms including mathematical functions with independent variables dependent on an input signal. A first set of known data is input to the initial mathematical representation to generate a output data. The output data of the initial mathematical representation and a second set of known data, correlated to the first set of known data, is fed to a comparator to generate error signals representing differences between output data and correlated members of the second set of known data. A parameter of the combination of terms is iteratively varied to produce a refined mathematical representation of the system until a measure of the error signals is reduced to a value wherein the output data of the refined mathematical representation over a desired range is equivalent to the second set of known data.

Abstract: A medical device inspection scope may include a cylindrical outer layer having a diameter of less than 2 millimeters or even less than 1 millimeter, a cylindrical inner layer disposed concentrically within the outer layer, a circumferential space between the outer layer and the inner layer, and an inner lumen formed by the inner layer. The scope also includes multiple light transmitting fibers disposed in the circumferential space and a camera module disposed in the inner lumen. The light fibers may be designed to transmit illumination light and ultraviolet light. The scope may also optionally include a stiffening member.

Abstract: 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.

Abstract: 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

Abstract: Disclosed are systems and methods for operations and maintenance (O&M) systems for controlling the operation of a second system, wherein the second system includes a plurality of objects (i.e., components and/or subsystems). The O&M system comprises means for evaluating at least one of the plurality of objects using a first model to produce a first prediction of a characteristic of the object; evaluating at least one of the plurality of objects using a second model to produce a second prediction of the characteristic of the object, the second model being dissimilar to the first model; generating a final prediction of the characteristic of the object as a function of dynamic weightings of the first prediction and the second prediction; and, executing an operational or maintenance decision with respect to the second system as a function of the final prediction of the characteristic of the at least one of the plurality of objects.

Abstract: A system and method for estimating a system state. The method includes making a first measurement and a second measurement of a value of a characteristic of a state of a system. The method includes constructing first filter measurement and time estimates after the second measurement coinciding with the first measurement including corresponding covariance matrices describing an accuracy of the first filter measurement and time estimates. The method includes constructing second filter measurement and time estimates coinciding with the second measurement including corresponding covariance matrices describing an accuracy of the second filter measurement and time estimates. The method includes constructing a smoothing estimate from the first and second filter measurement estimates.

Abstract: 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

Abstract: 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.

Abstract: 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 (?) 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.

Abstract: 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.

Abstract: 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.

Abstract: Architecture and related method of constructing a model of a real system, including constructing an initial neural-like representation of the real system with a combination of layers, the layers comprising mathematical functions including at least one independent variable; inputting a first set of known data to the initial neural-like representation to generate a corresponding set of output data, the known data comprising values for the at least one independent variable of the neural-like representation; feeding the corresponding set of output data of the initial neural-like representation and a second set of known data correlated to the first set of known data, to a comparator, the comparator generating error signals representing a difference between members of the set of output data and correlated members of the second set of known data; and, iteratively varying a weight parameter of at least one of the combination of terms comprising the initial neural-like representation to produce a refined neural-like r

Abstract: 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.

Abstract: A system and method for predicting a characteristic of an object in an artificial intelligence system. The method includes evaluating the object using a first model to produce a first prediction of a characteristic of the object. The object is evaluated using a second model to produce a second prediction of the characteristic of the object, the second model being dissimilar to the first model. A final prediction of the characteristic of the object is generated as a function of dynamic weightings of the first prediction and the second prediction.

Abstract: A system and method for estimating a system state. The method includes constructing a first estimate of a system state at a first time including a first covariance matrix describing an accuracy of the first estimate. A second estimate of the state is constructed at a second time, after the first time, including a second covariance matrix. A value of a characteristic of the system state is measured at the second time and the second estimate of the system state and the second covariance matrix are adjusted based on the value of the characteristic. A third estimate of the system state is constructed at a third time, before the second time, including a third covariance matrix describing an accuracy of the third estimate. A fourth estimate of the system state is constructed at a fourth time being after the second time.

Abstract: A system and method for constructing a mathematical model of a system. The method includes constructing an initial mathematical system representation with a combination of terms, the terms comprising mathematical functions including independent variables dependent on an input signal. A first set of known data is inputted to the initial mathematical representation to generate a corresponding set of output data. The corresponding set of output data of the initial mathematical representation and a second set of known data, correlated to the first set of known data, is fed to a comparator to generate error signals representing differences between output data and correlated members of the second set of known data.

Abstract: A system, apparatus and method for selecting a value for an independent variable that determines an operating state of a system described by a performance function. In one embodiment, the method includes establishing a range of values for the independent variable, selecting a number of values in the range of values to test the independent variable, and selecting random values within the range of values for the independent variable based on the number of values. The method also includes evaluating the performance function at the random values, and selecting the value of the independent variable from the random values that provides an extremum value for the performance function.