Spectral Analysis Using Deep Neural Network 2D Arrays
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Solution Overview
Problem
Existing methods for spectrum analysis, including those using deep neural networks, lack specific procedures for efficient and accurate analysis of light spectra from analysis objects, and multivariate analysis is not sufficient for high-efficiency and high-accuracy results.
Innovation Solution
A spectrum analysis apparatus and method utilizing a deep neural network that converts light spectra into two-dimensional array data, allowing for efficient and accurate analysis of analysis objects by training the network with reference objects and their mixtures, enabling classification and quantification of the analysis objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multivariate analysis is used for spectrum analysis, then the analysis can be performed with existing methods, but the efficiency and accuracy are insufficient for complex classifications and large datasets
Solution Approach 1:
The patent replaces traditional multivariate analysis methods with a deep neural network-based system. The deep neural network processes spectral data through multiple layers of nonlinear transformations, enabling it to capture complex patterns and relationships in the spectrum that traditional linear or simple nonlinear methods cannot handle, thereby achieving both high accuracy and efficiency
Solution Approach 2:
The patent transforms the spectral data into a two-dimensional array format that represents the spectrum as an image, where the x-axis represents wavelength and the y-axis represents intensity. This parameter transformation allows the deep neural network to process spectral data using convolutional neural network architectures designed for image processing, significantly improving computational efficiency and accuracy
2Productivity
If deep neural network is used for spectrum analysis, then efficiency and accuracy are expected to improve, but no specific procedure is provided for performing the analysis
Solution Approach 1:
The patent divides the spectrum analysis process into distinct sequential steps: (1) converting the spectrum into a two-dimensional array representation, (2) inputting the array into the deep neural network, (3) obtaining classification results, and (4) performing quantification. This segmentation makes the implementation straightforward and systematic, addressing the gap between having the right technology and knowing how to apply it
Solution Approach 2:
The patent introduces a two-dimensional array as an intermediary representation between the raw spectral data and the deep neural network. This array format serves as a bridge that transforms the spectral data into a format suitable for image processing techniques, making the integration of deep learning with spectrum analysis practical and implementable
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach enables highly efficient and accurate spectrum analysis, improving classification and quantification results compared to traditional multivariate analysis methods, even with complex classifications and large datasets.
Implementation Method 1
A spectrum of light generated in an analysis object has a shape corresponding to types or ratios of components contained in the analysis object. Therefore, the analysis object can be analyzed on the basis of the spectrum of light generated in the analysis object.
Data Source
AI summary
A spectrum analysis apparatus is an apparatus for analyzing an analysis object on the basis of a spectrum of light generated in the analysis object containing any one or two or more of a plurality of reference objects, and includes an array conversion unit, a processing unit, a learning unit, and an analysis unit. The array conversion unit generates two-dimensional array data on the basis of a spectrum of light generated in the reference object or the analysis object. The processing unit includes a deep neural network. The analysis unit causes the array conversion unit to generate the two-dimensional array data on the basis of the spectrum of light generated in the analysis object, inputs the two-dimensional array data to the deep neural network, and analyzes the analysis object on the basis of data output from the deep neural network.