Optical Fiber Sensing Model Optimization With Precomputed Signal Pairs
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Solution Overview
Problem
The computational amount required for optimizing optimization target models using machine learning models is high due to their statistical knowledge and high computational demands.
Innovation Solution
An optimization device and method that pre-stores input/output pairs of acoustic or vibration signals from optical fiber sensing to reduce computational load by optimizing an optimization target model using these pairs, incorporating statistical knowledge from machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is used to optimize an optimization target model by incorporating statistical knowledge, then optimization accuracy is improved, but computational amount increases
Solution Approach 1:
The patent pre-computes and stores input/output pairs from the machine learning model before the optimization process. By performing this action in advance, the system avoids the high computational cost of running the machine learning model during optimization, while still benefiting from its statistical knowledge for accurate optimization.
2Adaptability or versatility
If machine learning models are used for optimization, then statistical knowledge is incorporated, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary input/output pairs from the complex machine learning model and uses these extracted pairs for optimization. This approach separates the statistical knowledge extraction function from the optimization function, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
Instead of using the full machine learning model during optimization, the patent creates simplified copies in the form of pre-computed input/output pairs. These pairs capture the essential statistical relationships without the computational complexity of the original model.
Data Source
AI summary
An optimization device according to the present disclosure includes: at least one memory that stores a set of instructions; and at least one processor configured to execute the set of instructions, store in advance a plurality of first input/output pairs which are pairs of an input signal and an output signal of a machine learning model in a case where acoustic signals or vibration signals indicating a time-series change in acoustic waves or vibrations, which are observed through optical fiber sensing, are input to the machine learning model as input signals, in the at least one memory, and optimize an optimization target model using the plurality of first input/output pairs.