초록 ▼

A system for determining a leak rate of a device during an adiabatic cooling phase is provided. The system includes a leak rate training system receiving leak rate calibration data, such as pressure data or mass flow data for a device having a known leakage rate. The leak rate training system genera

A system for determining a leak rate of a device during an adiabatic cooling phase is provided. The system includes a leak rate training system receiving leak rate calibration data, such as pressure data or mass flow data for a device having a known leakage rate. The leak rate training system generates an adiabatic cooling model from the leak rate calibration data, such as by solving a finite difference equation for one or more unknown process variables that are dependent on the adiabatic cooling parameters of the device. The system further includes a leak rate detection system receiving the adiabatic cooling model and leak rate data and determining a leak rate component of the leak rate data using the adiabatic cooling model, such as by using the variable values determined through solving the finite difference model to interpolate between the leak rate calibration data that was gathered using known leakage rates.

대표청구항 ▼

A system for determining a leak rate of a device during an adiabatic cooling phase is provided. The system includes a leak rate training system receiving leak rate calibration data, such as pressure data or mass flow data for a device having a known leakage rate. The leak rate training system genera

A system for determining a leak rate of a device during an adiabatic cooling phase is provided. The system includes a leak rate training system receiving leak rate calibration data, such as pressure data or mass flow data for a device having a known leakage rate. The leak rate training system generates an adiabatic cooling model from the leak rate calibration data, such as by solving a finite difference equation for one or more unknown process variables that are dependent on the adiabatic cooling parameters of the device. The system further includes a leak rate detection system receiving the adiabatic cooling model and leak rate data and determining a leak rate component of the leak rate data using the adiabatic cooling model, such as by using the variable values determined through solving the finite difference model to interpolate between the leak rate calibration data that was gathered using known leakage rates. noise in each pixel-vector is modeled as shape vector multiplied by boundary discontinuity. The shape vector, varying with local image activity, is estimated by Minimum Mean Squared Error (MMSE) approach. Depending upon the shape vector, the corresponding blocky noise is eliminated in postprocessing of decoded images so that encoded images may be restored to their original images. third executable portion for encoding the tokens representing the high-order derivative of the electronic ink data points. 10. A computer program product according to claim 9 wherein the pen-enabled computing device further includes an electronic handwriting tablet capable of interacting with the writing stylus, and wherein said first executable portion captures a series of electronic ink data points based upon the position of the writing stylus with respect to the handwriting tablet over time.11. A computer program product according to claim 9 wherein said second executable portion determines the second-order derivative of the plurality of electronic ink data points.12. A computer program product according to claim 9 wherein said third executable portion Huffman encodes the high-order derivative.13. A pen-enabled computing device for representing a plurality of electronic ink data points comprising: a writing stylus; a handwriting capture interface, responsive to said stylus, for capturing a plurality of electronic ink data points based upon a position of the writing stylus; and a processing element, responsive to said handwriting capture interface, for determining a high-order derivative of the plurality of electronic ink data points, the high-order derivative comprising a derivative of at least the second order, wherein said processing element is also capable of representing the high order derivative of the plurality of electronic ink data points with respective tokens, and wherein said processing element is further capable of encoding the tokens representing the high-order derivative of the plurality of electronic ink data points. 14. A pen-enabled computing device according to claim 13 wherein said handwriting capture interface further comprises an electronic handwriting tablet capable sensing the relative position of said writing stylus.15. A pen-enabled computing device according to claim 13 wherein said processing element Huffman encodes the high-order derivative of the plurality of electronic ink data points.16. A pen-enabled computing device according to claim 13 wherein said processing element determines the second-order derivative of the plurality of electronic ink data points.17. A pen-enabled computing device according to claim 13 further comprising a user interface, wherein said processing element is further adapted to cooperate with said user interface to provide handwritten information to a user.18. A pen-enabled computing device according to claim 13 further comprising a storage device, responsive to said processing element, for storing the encoded high-order derivative of the plurality of electronic ink data points.19. A pen-enabled computing device according to claim 13 further comprising a transmitter, responsive to said processing element, for transmitting the encoded high-order derivative of the plurality of electronic ink data points.20. A method for capturing and reconstructing handwritten information, said method comprising: capturing a plurality of electronic ink data points based upon a position of a writing stylus; determining a high-order derivative of the plurality of electronic ink data points, wherein the high-order derivative comprises a derivative of at least the second order; representing the high order derivative of the plurality of electronic ink data points with respective tokens; and reconstructing the plurality of electronic ink data points based upon the high-order derivative, wherein said reconstructing occurs independent of all but one of the plurality of electronic ink data points that were captured. 21. A method according to claim 20 wherein said capturing comprises sensing the position of the writing stylus with respect to an electronic handwriting tablet of a pen-enabled computing device, and capturing a series of electronic ink data points based upon the position of the writing stylus with respect to the handwriting tablet over time.22. A method according t