General Electric Company

Florida State University Research Foundation, Inc.

Transportation IP Holdings, LLC

Olugbenga Moses Anubi

Lalit Keshav Mestha

Masoud Abbaszadeh

Justin Varkey John

Harry Kirk Mathews, Jr

James D Brooks

Weizhong Yan

Subhrajit Roychowdhury

Sumit Bose

Jonathan Carl Thatcher

Mustafa Tekin Dokucu

Aditya Kumar

Chaitanya Ashok Baone

Hema Kumari Achanta

Hullas Sehgal

Anup Menon

Ruijie Shi

Gayathri Seenumani

Santosh Sambamoorthy Veda

Marc Gavin Lindenmuth

Saikat Ray Majumder

Sree Rama Raju Vetukuri

Annartia Giani

Ashwin Vadivel

A recirculating aquaculture system (RAS) is disclosed, which includes a main tank, in which fish or shellfish are farmed; a first reactor fluidically connected to the main tank, wherein the first reactor is a batch reactor that operates under anoxic conditions; a second reactor fluidically connected to the main tank, wherein the second reactor is a moving bed biofilm reactor (MBBR); a feed stream fluidically connected to the main tank; and a data-driven controller operably connected to the first reactor, the second reactor, and the feed stream, wherein the data-driven controller is configured to bring and maintain the system (RAS) at a desired state.

Data-driven recirculating aquaculture system

An industrial asset may have monitoring nodes that generate current monitoring node values. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing fault. A dynamic, resilient estimator constructs, using normal monitoring node values, a latent feature space (of lower dimensionality as compared to a temporal space) associated with latent features. The system also constructs, using normal monitoring node values, functions to project values into the latent feature space. Responsive to an indication that a node is currently being attacked or experiencing fault, the system may compute optimal values of the latent features to minimize a reconstruction error of the nodes not currently being attacked or experiencing a fault. The optimal values may then be projected back into the temporal space to provide estimated values and the current monitoring node values from the abnormal monitoring node are replaced with the estimated values.

Dynamic, resilient sensing system for automatic cyber-attack neutralization

In some embodiments, an Unmanned Aerial Vehicle ('UAV') system may be associated with a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time that represent operation of the UAV system. An attack detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors. The attack detection computer platform may access an attack detection model having at least one decision boundary (e.g., created using a set of normal feature vectors a set of attacked feature vectors). The attack detection model may then be executed and the platform may transmit an attack alert signal based on the set of current feature vectors and the at least one decision boundary. According to some embodiments, attack localization and/or neutralization functions may also be provided.

Cyber-attack detection, localization, and neutralization for unmanned aerial vehicles

A convoy management system and method determine determining an inter-vehicle spacing in a convoy formed from two or more vehicles traveling together along one or more routes. Controllers onboard the two or more vehicles are instructed to automatically change movement of at least one of the vehicles in the convoy to maintain the inter-vehicle spacing. The inter vehicle spacing is dynamically changed during movement of the convoy along the one or more routes.

Vehicle convoy control system and method

The example embodiments are directed to a system and method for neutralizing abnormal signals in a cyber-physical system. In one example, the method includes receiving input signals comprising time series data associated with an asset and transforming the input signals into feature values in a feature space, detecting one or more abnormal feature values in the feature space based on a predetermined normalcy boundary associated with the asset, and determining an estimated true value for each abnormal feature value, and performing an inverse transform of each estimated true value to generate neutralized signals comprising time series data and outputting the neutralized signals.

Cyber-attack detection and neutralization

Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.

Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes

A dispatch optimization system leverages ambient and market forecast data as well as asset performance and parts-life models to generate recommended operating schedules for gas turbines or other power-generating plant assets that substantially maximize profit while satisfying parts-life constraints. The system generates operating profiles that balance optimal peak fire opportunities with optimal cold part-load opportunities within a maintenance interval or other operating horizon. During real-time operation of the assets, the optimization system can update the operating schedule based on actual market, ambient, and operating data. The system provides information that can assist operators in determining suitable conditions in which to cold part-load or peak-fire the assets in an optimally profitable manner without violating target life constraints.

Gas turbine dispatch optimizer real-time command and operations

According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.

Generic framework to detect cyber threats in electric power grid

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Tallahassee, FL, United States of America

Olugbenga Moses Anubi