Electric Power Research Institute Incorporated

Robert W Colley

Parvathy Bhaskaran

Ali Ipakchi

Alan J Filipski

Frank J Alexandro, Jr

Tad Krolik

Mostafa Khadem

Robert Flandrena

Steven Ruggiero

Mark Janney

A method and apparatus for predicting a signal value for a target element within a multi-element system is disclosed. The method includes modeling the multi-element system by defining fundamental physical relationships between the target element and other elements within the system. The resultant system model is in the form of a set of coupled non-linear differential equations. These differential equations are then approximated into linearized models about an operating point or series of operating points corresponding to the system behavior. The linearized differential equations are then subjected to a coupling analysis. The coupling analysis is employed to determine dynamic coupling between instruments. The coupling analysis assesses the degree of observability of the system and associated elements. The coupling analysis may be based upon observability tests, gramian analyses, or modal analyses. Based upon the coupling analysis, coupled elements are selected. The coupled elements correspond to system elements which are strongly coupled to the target element. A neural network is then trained using previous process values corresponding to the coupled elements. Thereafter, present operating system values corresponding to the coupled elements are fed to the trained neural network. The trained neural network processes the present operating system values to render a predicted value for the target element. This predicted value is then compared to the present system value to determine whether the target element is operating correctly.

Method and apparatus utilizing neural networks to predict a specified

An apparatus and method for recognizing written items including characters and symbols is disclosed. A scanner is utilized for scanning the characters and symbols. A recognition module is used to confidently identify the scanned characters and symbols. The recognition module includes a mechanism for recognizing characters. This mechanism includes a character recognition rule base which yields identified characters and unidentified characters. The unidentified characters are conveyed to neural networks for recognition. The recognition module also includes a mechanism for recognizing symbols. This mechanism includes a symbol recognition rule base which yields identified symbols and unidentified symbols. The unidentified symbols are conveyed to neural networks for recognition. The recognition module also includes a mechanism for context processing of the characters and symbols. The context processor utilizes a multilevel blackboard with a number of ascending levels. The blackboard combines characters and symbols into logical units. The blackboard also serves to verify the identity of the characters and symbols. The resultant data from the recognition module is placed in a metafile which is conveyed to a modification module. The modification module includes graphics editing software to edit the scanned characters and symbols. After editing, a translator is used to convert the scanned characters and symbols to a format suitable for a CAD system or data base.

Integrated method and apparatus for character and symbol recognition

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Menlo Park, CA, United States of America

Robert W Colley