Stream Sample Property Prediction With Adaptive Model Feedback
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Solution Overview
Problem
Existing systems lack flexibility in optimizing stream processing methods for samples with unknown properties, requiring human interaction and limited adaptability to varying input conditions.
Innovation Solution
A computer-implemented method using a pre-trained neural network to classify stream sample types and a sequential probabilistic model to predict and control processing parameters, with a blockchain for secure data storage and automatic re-training based on actual property values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network system is used to predict stream sample properties, then measurement capabilities are improved, but the system lacks flexibility for unknown input samples
Solution Approach 1:
The system performs preliminary classification of incoming stream samples using image recognition before processing. By pre-categorizing samples into known types based on visual characteristics, the system can then apply appropriate prediction models for each class, enabling accurate predictions for known sample types while maintaining the ability to handle unknown samples through the classification framework.
Solution Approach 2:
The system dynamically adjusts its operational parameters based on the classified sample type. Different neural network models or prediction algorithms are selected and applied according to the identified sample category, allowing the system to optimize its prediction accuracy for each specific sample type while maintaining overall system flexibility.
2Measurement precision
If manual calibration of neural network parameters is performed, then prediction accuracy is improved, but automation is reduced
Solution Approach 1:
The neural network system performs automatic calibration and optimization of its parameters through self-learning mechanisms. The system uses the classified sample data to continuously refine its prediction models without requiring manual intervention, thereby maintaining high prediction accuracy while achieving full automation of the calibration process.
Solution Approach 2:
The system implements feedback loops where prediction results are continuously evaluated and used to automatically adjust neural network parameters. This closed-loop control enables the system to self-optimize its accuracy over time based on actual performance data, eliminating the need for manual calibration while maintaining or improving prediction quality.
Data Source
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Figure 2B~4B
AI summary
A computer system (100), computer-implemented method and computer program product for evaluating a property of a physical stream sample is disclosed. An initial state image (ISI1) of said stream sample (S_b) at the beginning of the stream sample processing method (200) is received. A pre-trained neural network (120) determines a sample type (ST1) with a corresponding confidence value (CV1) for said stream sample (S_b). A probabilistic model (140) of the stream sample processing method predicts a target property value (PVt1) of the stream sample. A measuring result (MVe) for an actual property value (PVa1) of the processed stream sample (S_e) at the end of the sample processing method (200) is obtained. The actual property value (PVa1) is mapped backwards through the sequential probabilistic model (140) to the corresponding initial state image (ISI1). The sequential probabilistic model (140) or the neural network (120) or both are updated based on the actual property value.