David C Haas

US Patent:

6466888, Oct 15, 2002

Filed:

Aug 26, 1999

Appl. No.:

09/383624

Inventors:

Kelly McCool - University Park MD
David Haas - North Potomac MD

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G06F 1500

US Classification:

702144, 706 17

Abstract:

Input parameters which correspond to operational flight data of an aircraft within a predetermined flight domain, are defined through measured variable state parameters generated during aircraft flight, utilizing a neural network trained by exemplars corresponding to such variable state parameters and reference information on the aircraft for data processing of real time values of the variable state parameter measurement to calculate values of the input parameters and provide a corresponding output as a reliable estimate of aircraft flight data such as airspeed, sideslip and angle of attack.

US Patent:

6898584, May 24, 2005

Filed:

Oct 26, 2000

Appl. No.:

09/696299

Inventors:

Kelly McCool - Upper Marlboro MD, US
David J. Haas - North Potomac MD, US
Gene L. Barndt - Manassas VA, US

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G05B013/00

US Classification:

706 23

Abstract:

An algorithm housed within a rotorcraft, controls real time generation of signals reflecting sensor measurement of plural parameters in accordance with a programmed sequence operationally intermapped pursuant to a linear or non-linear equation with calculations therefrom of estimates to provide an accurate determination of flight usage from which to evaluate and detect excessive damage with respect to components of the aircraft.

US Patent:

7624080, Nov 24, 2009

Filed:

Feb 25, 2006

Appl. No.:

11/362595

Inventors:

Miguel A. Morales - Leesburg VA, US
David J. Haas - North Potomac MD, US

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G06F 17/00
G06F 3/08
G06N 5/00

US Classification:

706 20, 706 22, 706 25, 706 61

Abstract:

As the present invention's adaptation process is typically practiced, an observation made by one or more sensing element(s) is classified as being either recognized or unrecognized in the context of a knowledge base. If the observation is classified as being recognized and consistent, then the observation is assimilated into the knowledge base; otherwise, it is not assimilated. If the observation is classified as being unrecognized, then the observation is classified as being uncorroborated in the context of the knowledge base. Prior to being classified as being uncorroborated, the unrecognized observation is categorized in the context of the knowledge base and is associated with an outcome in terms of relationship between/among physical parameters. At the time that corroboration is determined, the observation (originally unrecognized) and its categorization-related and association-related information are assimilated into the knowledge base.

US Patent:

59012722, May 4, 1999

Filed:

Oct 24, 1996

Appl. No.:

8/736176

Inventors:

Carl G. Schaefer - Woodbridge VA
Kelly M. McCool - University Park MD
David J. Haas - North Potomac MD

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G06F 1518

US Classification:

395 22

Abstract:

The invention is directed to means, utilizing a neural network, for estimating helicopter airspeed at speeds below about 50 knots using only fixed system parameters as inputs to the neural network. The system includes: means for entering at least one initial parameter; means for measuring, in a nonrotating reference frame associated with the helicopter, a plurality of variable state parameters generated during flight of the helicopter; means for determining a plurality of input parameters based on the at least one initial parameter and the plurality of variable state parameters and for generating successive signals representing the input parameters; at least one equation representing a nonlinear input-output relationship between the input parameters and airspeed; memory means for storing the at least one equation and for successively receiving and storing signals from the determining means; and processing means responsive to signals received from the memory means for generating airspeed information based on the input parameters and the at least one equation.

US Patent:

57516090, May 12, 1998

Filed:

Oct 24, 1996

Appl. No.:

8/740067

Inventors:

Carl G. Schaefer - Woodbridge VA
Kelly M. McCool - University Park MD
David J. Haas - North Potomac MD

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G01C 2110

US Classification:

364565

Abstract:

The invention is directed to a method, utilizing a neural network, for estimating helicopter airspeed in the low airspeed flight range of below about 50 knots using only fixed system parameters as inputs to the neural network. The method includes the steps of: (a) defining input parameters derivable from variable state parameters generated during flight of the helicopter and measurable in a nonrotating reference frame associated with the helicopter; (b) determining the input parameters and a corresponding helicopter airspeed at a plurality of flight conditions representing a predetermined low airspeed flight domain of the helicopter; (c) establishing a learned relationship between the determined input parameters and the corresponding helicopter airspeed wherein the relationship is represented by at least one nonlinear equation; (d) storing the at least one nonlinear equation in a memory onboard the helicopter; (e) measuring real time values of the variable state parameters during low airspeed flight of the helicopter; (f) calculating real time values of the input parameters; (g) storing the real time values of the input parameters in the memory; (h) processing the real time values of the input parameters in accordance with the at least one nonlinear equation to determine real time airspeed; and (i) displaying the real time airspeed.

US Patent:

59873973, Nov 16, 1999

Filed:

Mar 13, 1998

Appl. No.:

9/042045

Inventors:

Kelly McCool - University Park MD
David Haas - North Potomac MD

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G01C 2110

US Classification:

702173

Abstract:

The invention is directed to a helicopter health and usage monitoring system utilizing a neural network for estimating gross weight and center of gravity location from measured flight condition parameter inputs; and includes means for measuring a plurality of variable flight condition parameters during flight of the helicopter; memory means for successively receiving and storing parameter input signals as well as estimates of gross weight and center of gravity location; and processing means responsive to the signals received from the measurement means for generating the gross weight and center of gravity location estimates.

US Patent:

58901019, Mar 30, 1999

Filed:

Oct 22, 1997

Appl. No.:

8/955970

Inventors:

Carl G. Schaefer - Woodbridge VA
Kelly M. McCool - University Park MD
David J. Haas - North Potomac MD

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G01C 2110

US Classification:

702144

Abstract:

The invention is directed to a system, utilizing a neural network, for estimating helicopter airspeed in the low airspeed flight range of below about 50 knots using only fixed system parameters as inputs to the neural network. The method includes the steps of: (a) defining input parameters derivable from variable state parameters generated during flight of the helicopter and measurable in a nonrotating reference frame associated with the helicopter; (b) determining the input parameters and a corresponding helicopter airspeed at a plurality of flight conditions representing a predetermined low airspeed flight domain of the helicopter; (c) establishing a learned relationship between the determined input parameters and the corresponding helicopter airspeed wherein the relationship is represented by at least one nonlinear equation; (d) storing the at least one nonlinear equation in a memory onboard the helicopter; (e) measuring real time values of the variable state parameters during low airspeed flight of the helicopter; (f) calculating real time values of the input parameters; (g) storing the real time values of the input parameters in the memory; (h) processing the real time values of the input parameters in accordance with the at least one nonlinear equation to determine real time airspeed; and (i) displaying the real time airspeed.