Knowledge generation support system, parameter search method and program product

• US 20070093987A1
• Filed: 10/07/2005
• Published: 04/26/2007
• Est. Priority Date: 10/07/2005
• Status: Abandoned Application

• Current Assignee: Omron Corporation
• Original Assignee: Omron Corporation
• Inventors: Iida, Toyoo
• Application Number:
  US11/245,592
• Publication Number:
  US 20070093987A1
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Abstract

A system is disclosed which can easily search and determine the effective feature amount suitable for determining the normality/abnormality of an object to be inspected in an inspection/diagnosis apparatus and the various parameters for calculating the effective feature amount. A parameter search unit searches for the various parameters used for calculating the feature amount. A feature amount calculation unit calculates a plurality of feature amounts based on the various parameters searched by the parameter search unit from a given sample data including the normal and abnormal data. An assessment unit outputs the excellence of the various parameters as an assessment value from the result of calculation of the feature amount determined by the feature amount calculation unit. The parameter search unit searches the various parameters again based on the assessment result of the assessment unit thereby to determine an effective feature amount a high assessment value and the various parameters for the particular effective feature amount at the same time.

Claims

1. A knowledge generation support system for an inspection/diagnosis apparatus to determine whether an object of inspection is normal or abnormal based on feature amount data obtained by a filtering process and a feature amount extraction process executed on acquired measurement data, wherein an effective feature amount suitable for the object of inspection and various parameters for calculating the effective feature amount are determined, the system comprising:

a search unit for searching for various parameters to search for the various parameters for calculating a feature amount,

a feature amount calculation unit for calculating a plurality of feature amounts based on the various parameters obtained in the search unit from a given sample data containing normal and abnormal data, and

an assessment unit for outputting, as an assessment value, an excellence of the various parameters from a result of assessment of the feature amount determined by the feature amount calculation unit,

wherein the search unit searches the various parameters again based on the assessment result of the assessment unit thereby to determine an effective feature amount high in assessment value and the various parameters for the particular effective feature amount.

2. A knowledge generation support system according to claim 1,

wherein methods of searching the various parameters in the assessment unit include:

(1) A method in which a degree of ability to separate the normality and abnormality is emphasized, and

(2) A method in which a number of the feature amounts separable is emphasized,

wherein one of the methods is selectively executable, and in accordance with the selected search method, one type described below is determined,

(1) An effective feature amount of which normality and abnormality can be separated from each other most effectively and various parameters for calculating the effective feature amount, and

(2) A plurality of effective feature amounts for separating normality and abnormality from each other and various parameters for calculating the effective feature amounts.

3. A knowledge generation support system according to claim 1,

wherein the sample data is divided into the abnormal data and the normal data for the same abnormality type.

4. A knowledge generation support system according to claim 1,

wherein the search unit searches for the optimum various parameters by executing crossover, mutation and selection operation until desired conditions are met, using a generic algorithm for individuals with the various parameters coded, and

wherein genes of each coded individual are blocked by function and manifestation or concealment of the gene character is controlled by block.

5. A knowledge generation support system according to claim 1,

comprising a function in which in a case where the search with the various parameters using all the sample data satisfies desired conditions, data that can be estimated as an erroneous determination is extracted from the sample data, and in a case where the assessment value obtained by determining the various parameters using the sample reconstructed while the data estimated as an erroneous determination is regarded as an erroneous determination is higher than the assessment value before the reconstruction, the data estimated as an erroneous determination is finally judged as an erroneous determination data.

6. A knowledge generation support system according to claim 5,

wherein the reconstruction is execution of at least one of a process to delete the data estimated as an erroneous determination, a process to replace with sample group of the opposite determination results and a process to replace with a sample group of a different determination result.

7. A knowledge generation support system according to claim 5,

wherein the data finally judged as an erroneous determination is updated to the correct sample data, while the search is executed by the search unit.

8. A parameter search method for a knowledge generation support system for determining an effective feature amount suitable to an object of inspection and calculating the effective feature amount for an inspection/diagnosis apparatus to determine whether the object of inspection is normal or abnormal based on feature amount data acquired by subjecting acquired measurement data to a filtering process and a feature amount extraction process, comprising the steps of:

calculating a plurality of feature amounts based on various parameters set by a feature amount calculation unit with regard to given sample data containing normal and abnormal data,

calculating an assessment value indicating an excellence of the various parameters from a result of calculation of the feature amount determined by the feature amount calculation unit,

searching the various parameters again based on the calculated assessment result,

repeatedly calculating the feature amount and the assessment value based on the various parameters searched, and

determining an effective feature amount high in assessment value meeting set search finish conditions and the various parameters of the effective feature amount at the same time.

9. A parameter search method according to claim 8,

wherein two types of methods are prepared for searching the various parameters including a first method in which a degree of ability to separate the normality and abnormality from each other is emphasized and a second method in which a number of the feature amounts that can be separated is emphasized, and the first search method or the second search method thus set is executed thereby to determine either

(1) an effective feature amount that can separate the normality and abnormality from each other most effectively and various parameters for calculating the effective feature amount, or

(2) a plurality of effective feature amounts for separating the normality and abnormality from each other and various parameters for calculating the effective feature amounts.

10. A program product for determining an effective feature amount suitable for an object of inspection in an inspection/diagnosis apparatus for determining whether the object of inspection is normal or abnormal based on feature amount data obtained by subjecting acquired measurement data to a filtering process and a feature amount extraction process, the program product having a program portion comprising the steps of:

calculating a plurality of feature amounts based on various parameters set by a feature amount calculation unit from a given sample data including the normal data and the abnormal data,

calculating an assessment value indicating an excellence of the various parameters from a result of calculation of the feature amount determined by the feature amount calculation unit,

searching the various parameters again based on the calculated assessment result,

repeating the feature amount calculation and the assessment value calculation based on the various parameters searched until set search finish conditions are met, and determining the effective feature amount high in assessment value meeting the set search finish conditions and the various parameters of the effective feature amount at the same time.

11. A program product according to claim 10,

comprising a program portion for executing selected one of a first method and a second method as a process for searching the various parameters in accordance with the selected search method, the first method being such that a degree of ability to separate the normality and abnormality from each other is emphasized, the second method being such that a number of the separable feature amounts is emphasized.