Spectral Material Identification Accuracy Estimation Without Database Access
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Solution Overview
Problem
Existing material identification technologies face challenges in maintaining accuracy without relying on a high-accuracy material database, especially when different business operators handle the inspection data, leading to increased costs and confidentiality issues.
Innovation Solution
A system that analyzes the relationship between feature amount errors and identification accuracy, using a model function to calculate standard deviations and generate correlation information for accurate material identification, allowing inspection regardless of database accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-accuracy material database is used for material identification, then identification accuracy is improved, but data acquisition cost and confidentiality requirements increase
Solution Approach 1:
The patent introduces an accuracy degree calculation unit as an intermediary that calculates the accuracy degree of material identification results without requiring direct access to the confidential material database. This intermediary component enables inspection businesses to assess identification reliability while maintaining database confidentiality and reducing data acquisition costs.
Solution Approach 2:
The patent segments the material identification system into distinct functional components: a spectral analysis unit that processes spectral data, a material identification unit that performs identification, and an accuracy degree calculation unit that evaluates reliability. This segmentation allows the inspection business to operate independently with lower data acquisition requirements while maintaining identification accuracy through the accuracy evaluation mechanism.
2Adaptability or versatility
If different business operators handle inspection data and material database separately, then operational flexibility is improved, but identification accuracy may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the accuracy degree calculation unit continuously evaluates the reliability of material identification results and provides this information back to the inspection process. This feedback loop enables different business operators to work independently while maintaining identification accuracy through real-time accuracy assessment and adjustment.
3Loss of information
If material identification is performed without access to confidential material database, then data acquisition cost is reduced, but identification reliability may worsen
Solution Approach 1:
The patent enables the inspection business to perform self-service material identification by calculating accuracy degrees based on spectral data analysis alone, without requiring access to the confidential material database. The system uses spectral feature amounts and their standard deviations to independently evaluate identification reliability, allowing the inspection business to operate autonomously with reduced data acquisition costs while maintaining identification reliability through the self-calculating accuracy mechanism.
Data Source
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AI summary
To address the above problem, the present invention is provided with: a spectral analysis unit 3 for receiving a plurality of items of spectral data acquired by a measurement device 2 with regards to a material according to a prescribed measurement condition, and calculating spectral feature amounts of the spectral data using a model function; a standard deviation acquisition unit 4 for estimating, on the basis of the spectral feature amounts, the standard deviation between the model function and the values actually measured by the measurement device; and a feature amount error-identification accuracy degree relationship analysis unit 6 for calculating, for each of the spectral feature amounts, an identification accuracy degree in relation to identifying the material, using spectral feature amount-material identifier correlation information indicating the correspondence relationship for the material identifier in question, and generating, using the identification accuracy degree and the standard deviation, feature amount error-identification accuracy degree correlation information indicating the correlation relationship between the identification accuracy degree and the spectral feature amount error for the material.