東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Data mining methods for quantitative...

Torabi, Keivan.

Linked to FindBook

Google Book

Amazon

博客來

Data mining methods for quantitative in-line image monitoring in polymer extrusion.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data mining methods for quantitative in-line image monitoring in polymer extrusion./
Author:	Torabi, Keivan.
Description:	240 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3279.
Contained By:	Dissertation Abstracts International66-06B.
Subject:	Engineering, Chemical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR02705
ISBN:	0494027053

Data mining methods for quantitative in-line image monitoring in polymer extrusion.
Torabi, Keivan.

Data mining methods for quantitative in-line image monitoring in polymer extrusion. - 240 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3279.

Thesis (Ph.D.)--University of Toronto (Canada), 2005.

This thesis is based upon the hypothesis that data mining methods, when combined with existing and new monitor interpretation methods, can enable in-line image monitoring of polymer film extrusion to be used for high throughput analysis. Data mining refers to a wide variety of methods available for deriving information from data, especially large quantities of data. The specialized camera system used to obtain the images was a "scanning particle monitor" that can obtain images across the direction of the flow of the polymer melt. The two objectives necessary to show the validity of the hypothesis were accomplished: automated quantitative image interpretation methods for detecting the presence, number, size and concentration of contaminant particles were devised and experimentally verified using thousands of images; these methods were combined and implemented to enable the interpretation to adapt to dynamic environments. A new, very advantageous method of image thresholding was derived (termed "maximizing the minimum particle size"). Bayesian classification was found to be superior to other classification methods when both error rate and false negative rate was considered for detecting images containing contaminant particles. Decision tree was found to be superior to other classification methods for distinguishing in-focus from blurry images of particles. The "intelligent learning machine" method, based upon supervised learning to achieve adaptability, was used with Bayesian classification and the new thresholding method to provide the needed adaptive, real-time interpretation algorithm.

ISBN: 0494027053Subjects--Topical Terms:

1018531
Engineering, Chemical.

Data mining methods for quantitative in-line image monitoring in polymer extrusion.
LDR:02428nmm 2200253 4500 001 1814908
005 20060719122846.5
008 130610s2005 eng d
020 $a 0494027053
035 $a (UnM)AAINR02705
035 $a AAINR02705
040 $a UnM $c UnM
100 1 $a Torabi, Keivan. $3 1904351
245 1 0 $a Data mining methods for quantitative in-line image monitoring in polymer extrusion.
300 $a 240 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3279.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2005.
520 $a This thesis is based upon the hypothesis that data mining methods, when combined with existing and new monitor interpretation methods, can enable in-line image monitoring of polymer film extrusion to be used for high throughput analysis. Data mining refers to a wide variety of methods available for deriving information from data, especially large quantities of data. The specialized camera system used to obtain the images was a "scanning particle monitor" that can obtain images across the direction of the flow of the polymer melt. The two objectives necessary to show the validity of the hypothesis were accomplished: automated quantitative image interpretation methods for detecting the presence, number, size and concentration of contaminant particles were devised and experimentally verified using thousands of images; these methods were combined and implemented to enable the interpretation to adapt to dynamic environments. A new, very advantageous method of image thresholding was derived (termed "maximizing the minimum particle size"). Bayesian classification was found to be superior to other classification methods when both error rate and false negative rate was considered for detecting images containing contaminant particles. Decision tree was found to be superior to other classification methods for distinguishing in-focus from blurry images of particles. The "intelligent learning machine" method, based upon supervised learning to achieve adaptability, was used with Bayesian classification and the new thresholding method to provide the needed adaptive, real-time interpretation algorithm.
590 $a School code: 0779.
650 4 $a Engineering, Chemical. $3 1018531
650 4 $a Chemistry, Polymer. $3 1018428
690 $a 0542
690 $a 0495
710 2 0 $a University of Toronto (Canada). $3 1017674
773 0 $t Dissertation Abstracts International $g 66-06B.
790 $a 0779
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR02705