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JP-2026074764-A - Information processing device, information processing method, and program

JP2026074764AJP 2026074764 AJP2026074764 AJP 2026074764AJP-2026074764-A

Abstract

[Problem] To provide an information processing device and method that reduces differences in spectral measurement conditions at a lower cost. [Solution] In an information processing device, the control unit includes an acquisition unit that acquires multiple spectra, a first correspondence generation unit that generates correspondence data representing the correspondence between spectra for the acquired multiple spectra, a space generation unit that compresses the dimensions in the correspondence data using a predetermined algorithm and generates a feature space which is a space containing the compressed data, a data addition unit that adds one or more virtual data to the feature space, a second correspondence generation unit that restores the added virtual data to the same dimensions as the correspondence data generated by the first correspondence generation unit and generates further correspondence data, an additional acquisition unit that acquires one or more additional spectra, and a virtual spectrum generation unit that generates one or more virtual spectra using the one or more additional spectra and the correspondence data generated by the first correspondence generation unit and the second correspondence generation unit, respectively. [Selection Diagram] Figure 1

Inventors

  • 蔦 瑞樹

Assignees

  • 国立研究開発法人農業・食品産業技術総合研究機構

Dates

Publication Date
20260507
Application Date
20241021

Claims (7)

  1. An acquisition unit that acquires multiple spectra, A first correspondence generation unit generates correspondence data representing the correspondence between the spectra for the plurality of spectra acquired by the acquisition unit, A space generation unit compresses the dimensions of the aforementioned correspondence data using a predetermined algorithm and generates a feature space which is a space containing the compressed data, A data addition unit that adds one or more virtual data to the feature space, A second correspondence generation unit restores the added virtual data to the same dimension as the correspondence data generated by the first correspondence generation unit, and further generates the correspondence data. An additional acquisition unit that acquires one or more additional spectra, A virtual spectrum generation unit generates one or more virtual spectra using the one or more additional spectra and the correspondence data generated by the first correspondence generation unit and the second correspondence generation unit, respectively. An information processing device equipped with the following features.
  2. Each of the one or more additional spectra is associated with a predetermined target variable. The virtual spectrum generation unit, when generating one or more virtual spectra from one spectrum, associates the predetermined target variable with the one or more virtual spectra. The information processing apparatus according to claim 1.
  3. The aforementioned correspondence data is a difference spectrum representing the difference between spectra, or a conversion coefficient that converts spectra between spectra. The information processing apparatus according to claim 1.
  4. The aforementioned predetermined algorithm is principal component analysis, kernel principal component analysis, independent component analysis, factor analysis, multidimensional scaling, non-negative matrix factorization, t-SNE (t-distributed Stochastic Neighbor Embedding), UMAP (Uniform Manifold Approximation and Projection), or an autoencoder. The information processing apparatus according to claim 1.
  5. Data including one or more virtual spectra generated by the information processing device described in claim 2, which is training data for training a machine learning model that takes spectra as input and an objective variable as output.
  6. The acquisition process for obtaining multiple spectra, A first correspondence generation process generates correspondence data representing the correspondence between the spectra for the plurality of spectra acquired in the acquisition process, A spatial generation process that compresses the dimensions of the aforementioned correspondence data using a predetermined algorithm and generates a feature space which is a space containing the compressed data, A data addition process that adds one or more virtual data to the feature space, A second correspondence generation process that restores the added virtual data to the same dimension as the correspondence data generated in the first correspondence generation process, and further generates the correspondence data. An additional acquisition process to acquire one or more additional spectra, A virtual spectrum generation process that generates one or more virtual spectra using the one or more additional spectra and the correspondence data generated in the first correspondence generation process and the second correspondence generation process, Information processing methods, including those mentioned above.
  7. On the computer, The acquisition process for obtaining multiple spectra, A first correspondence generation process generates correspondence data representing the correspondence between the spectra for the plurality of spectra acquired in the acquisition process, A spatial generation process that compresses the dimensions of the aforementioned correspondence data using a predetermined algorithm and generates a feature space which is a space containing the compressed data, A data addition process that adds one or more virtual data to the feature space, A second correspondence generation process that restores the added virtual data to the same dimension as the correspondence data generated in the first correspondence generation process, and further generates the correspondence data. An additional acquisition process to acquire one or more additional spectra, A virtual spectrum generation process that generates one or more virtual spectra using the one or more additional spectra and the correspondence data generated in the first correspondence generation process and the second correspondence generation process, A program that executes something.

Description

This invention relates to an information processing device, learning data, an information processing method, and a program. Techniques are known to mitigate spectral differences that may arise due to differences in measurement conditions. Examples of differences in measurement conditions include individual differences in the measuring equipment used, differences in the origin of the crop, differences in the type of crop, and year-to-year differences in the crop. For example, Non-Patent Document 1 discloses a technique for creating an estimation model that can be applied regardless of the measuring equipment. Norgaard, Lars et al. "Artificial Neural Networks and Near Infrared Spectroscopy - A case study on protein content in whole wheat grain", A White Paper from FOSS Issue 1, 2013 This is a block diagram showing an example configuration of an information processing device according to the present invention.This figure shows an example of spectral division according to the present invention.This figure shows an example of a difference spectrum according to the present invention.This figure shows an example of principal component analysis in a difference spectrum according to the present invention.This figure shows an example of adding virtual data according to the present invention.This figure shows an example of a difference spectrum after adding virtual data according to the present invention.This figure shows an example of generating a virtual spectrum according to the present invention.This is a flowchart illustrating an example of the flow of the information processing method according to the present invention.This figure shows an example of a regression coefficient according to the present invention.This figure shows an example of spectral transformation coefficients according to the present invention.This figure shows an example of principal component analysis in spectral transformation coefficients according to the present invention.This figure shows an example of adding virtual data according to the present invention.This figure shows an example of generating a virtual spectrum according to the present invention.This figure shows an example of estimation results using the estimation model according to the present invention.This figure shows an example of adding virtual data according to the present invention.This figure shows an example of estimation results using the estimation model according to the present invention. [Embodiment 1] One embodiment of the present invention will be described in detail below. (Configuration of Information Processing Device 1) The configuration of the information processing device 1 will be described with reference to Figure 1. Figure 1 is a block diagram showing an example of the configuration of the information processing device 1. The information processing device 1 includes, for example, a control unit 10, a storage unit 20, a communication unit 30, and an input/output unit 40, as shown in Figure 1. (Control unit 10) The control unit 10 controls all parts of the information processing device 1. For example, as shown in Figure 1, the control unit 10 includes an acquisition unit 11, a first correspondence generation unit 12, a space generation unit 13, a data addition unit 14, a second correspondence generation unit 15, an additional acquisition unit 16, and a virtual spectrum generation unit 17. (Acquisition unit 11) The acquisition unit 11 acquires multiple spectra. A spectrum is obtained by decomposing information or a signal into its (continuous or discontinuous) components and arranging each component by its "value (magnitude, intensity, frequency, etc.)". Specific examples of spectra acquired by the acquisition unit 11 include spectral spectra, frequency spectra, electromagnetic spectra, and mass spectra. For example, the spectra acquired by the acquisition unit 11 may be those measured using a measuring device. For example, the information processing device 1 and the measuring device may be connected via a network N, etc., as described later. Furthermore, for example, the information processing device 1 may further include a configuration for measuring the spectrum provided by the aforementioned measuring device. The multiple spectra acquired by the acquisition unit 11 may be measured using, for example, one or more measuring devices. That is, the acquisition unit 11 may acquire spectra measured by each of the multiple measuring devices, or it may acquire multiple spectra measured by a single measuring device. Figure 2 shows an example of segmentation in a spectrum measured using a measuring device. For example, the measuring device may be configured with one master unit and multiple slave units, as shown in Figure 2. The spectra measured using the measuring device may be divided into, for example, a conversion set, a training set (parent unit only), and a validation set, as shown in Figure 2. The conversion set consists of spectra used to convert spectra bet