Kathleen D Keay

US Patent:

7289863, Oct 30, 2007

Filed:

Aug 18, 2005

Appl. No.:

11/206675

Inventors:

Joseph D. Arruda - Swansea MA, US
Kathleen D. Keay - Franklin MA, US
Glen F. R. Gilchrist - Danvers MA, US

Assignee:

Brooks Automation, Inc. - Chelmsford MA

International Classification:

G06F 14/00
G01M 3/32
G01M 3/04

US Classification:

700110, 700121, 702 51, 73 40, 73 407

Abstract:

In one embodiment according to the invention, there is disclosed a method of identifying a source of a vacuum quality problem in a vacuum environment associated with a tool. The method comprises gathering and storing vacuum environment data; identifying an anomaly within the vacuum environment; determining a tool component operating state when the anomaly likely occurred; and determining the source of the vacuum quality problem based on a state of the vacuum environment when the anomaly likely occurred and the tool component operating state when the anomaly likely occurred.

US Patent:

53734564, Dec 13, 1994

Filed:

Nov 2, 1992

Appl. No.:

7/976349

Inventors:

David J. Ferkinhoff - New Bedford MA
Kai F. Gong - Pawtucket RI
Kathleen D. Keay - Fairhaven MA
Steven C. Nardone - Narragansett RI

Assignee:

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

International Classification:

G01S 766
G06F 1520

US Classification:

364574

Abstract:

A system for providing an iterative method of assessing accuracy of selec models of physical phenomena and for determining selection of alternate models in response to a data sequence in the presence of noise. Initially, a residual sequence is generated reflecting difference values between in response to said data sequence and an expected data sequence as would be represented by a selected model. Feature estimate values of a plurality of predetermined data features in the residual sequence are then generated. In response to the feature estimate values, a threshold value is generated for each feature at an estimated ratio of data to noise. Probability values are generated in response to the threshold value, representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable. Finally, a model is selected in response to the probability values for use during a subsequent iteration.

US Patent:

55814905, Dec 3, 1996

Filed:

Dec 9, 1994

Appl. No.:

8/353853

Inventors:

David J. Ferkinhoff - Middletown RI
John G. Baylog - Tiverton RI
Kai F. Gong - Pawtucket RI
Kathleen D. Keay - Fairhaven MA
Sherry E. Hammel - Little Compton RI

Assignee:

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

International Classification:

G01S 766
G06F 1900

US Classification:

364578

Abstract:

A system for providing an iterative method of assessing accuracy of selected models of physical phenomena and for determining selection of alternate models in response to a data sequence in the presence of noise. Initially, a residual sequence is generated reflecting difference values between in response to said data sequence and an expected data sequence as would be represented by a selected model. Feature estimate values of a plurality of predetermined data features in the residual sequence are then generated. In response to the feature estimate values, a threshold value is generated for each feature at an estimated ratio of data to noise power. Probability values are generated in response to the threshold value, representing the likelihood that the feature exists in the data sequence, does not exist in the data sequence, and that the existence or non-existence in the data sequence is not determinable, along with an amplitude probability value indicating the belief of the amplitude of the respective feature in the data sequence. Probability values are generated in response to the feature existence and amplitude probability values, representing the likelihood that various modelling hypotheses are represented by the observed features, or are not ruled out by the observed features in the presence of the given noise level. Finally, a model is selected in response to the probability values for use during a subsequent iteration.

US Patent:

54693748, Nov 21, 1995

Filed:

Jun 23, 1993

Appl. No.:

8/083404

Inventors:

Marcus L. Graham - North Kingstown RI
John F. MacDonald - Westport MA
Kai F. Gong - Pawtucket RI
Kathleen D. Keay - Fairhaven MA

Assignee:

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

International Classification:

G06F 1700

US Classification:

364516

Abstract:

A system having a device for comparing incoming data with hypotheses prevsly formed from prior data for providing new hypotheses on target information. It has application when the data source is from either single or multiple targets. The incoming datum to the system forms new hypotheses assuming the incoming datum is invalid, forms new hypotheses assuming the new datum begins a new segment of information, and forms new hypotheses assuming the new datum is associated with segments in prior retained hypotheses. The one hypothesis of the thusly formed new hypotheses with the greatest likelihood of target information is then selected for further analyzation and the hypothesis selected and other hypotheses are retained for further processing with new incoming datum.

US Patent:

54714338, Nov 28, 1995

Filed:

Oct 18, 1994

Appl. No.:

8/324638

Inventors:

Sherry E. Hammell - Little Compton RI
Kai F. Gong - Pawtucket RI
Neil A. Jackson - North Kingston RI
Kathleen D. Keay - Fairhaven MA
John F. MacDonald - Westport MA

Assignee:

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

International Classification:

G01S 1566
G01S 1558

US Classification:

367118

Abstract:

A trajectory estimation system for estimating a trajectory of a target in sponse to a series of data items which generated in response to motion of the target. The trajectory estimation system includes a data segmentation means and a trajectory selection means. The data segmentation means processes the series of data items in accordance with a regression/multiple-hypothesis methodology to generate a plurality of segments, each having associated data items which have similar features. The trajectory selection means for processing said segments in accordance with a multiple-model hypothesis methodology to generate a corresponding statistically-supportable candidate trajectory motion estimate of target motion thereby to provide indicia of an overall trajectory of the target.