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Title: Binary classification of items of interest in a repeatable process

Abstract

A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest.

Inventors:
; ; ; ;
Publication Date:
Research Org.:
GM Global Technology Operations LLC, Detroit, MI (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1167030
Patent Number(s):
8,925,791
Application Number:
14/264,113
Assignee:
GM Global Technology Operations LLC (Detroit, MI) DOEEE
DOE Contract Number:
EE0002217
Resource Type:
Patent
Resource Relation:
Patent File Date: 2014 Apr 29
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Abell, Jeffrey A, Spicer, John Patrick, Wincek, Michael Anthony, Wang, Hui, and Chakraborty, Debejyo. Binary classification of items of interest in a repeatable process. United States: N. p., 2015. Web.
Abell, Jeffrey A, Spicer, John Patrick, Wincek, Michael Anthony, Wang, Hui, & Chakraborty, Debejyo. Binary classification of items of interest in a repeatable process. United States.
Abell, Jeffrey A, Spicer, John Patrick, Wincek, Michael Anthony, Wang, Hui, and Chakraborty, Debejyo. Tue . "Binary classification of items of interest in a repeatable process". United States. doi:. https://www.osti.gov/servlets/purl/1167030.
@article{osti_1167030,
title = {Binary classification of items of interest in a repeatable process},
author = {Abell, Jeffrey A and Spicer, John Patrick and Wincek, Michael Anthony and Wang, Hui and Chakraborty, Debejyo},
abstractNote = {A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 06 00:00:00 EST 2015},
month = {Tue Jan 06 00:00:00 EST 2015}
}

Patent:

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