Signal Pre-Processing Using Data-Driven Models and Graph Structures
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Solution Overview
Problem
Conventional data processing systems lack effective methods to bridge the gap between domain knowledge and data, leading to inefficient data preprocessing and analysis, particularly in handling complex, high-volume data from domains like healthcare and manufacturing, where the connection between knowledge and analytics is not adequately captured.
Innovation Solution
A processor-implemented method and system for signal pre-processing using data-driven models and domain-dependent model transformation, which learns a set of orthonormal vectors, determines the structure of dictionary atoms, integrates graph structures, and reconstructs signals to denoise and remove anomalies, incorporating domain knowledge to preserve essential features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing approaches are used that only look at input/output relations, then the processing is simple and fast, but the connection between domain knowledge and analytics is not captured, leading to less accurate and meaningful analysis
Solution Approach 1:
The patent introduces domain knowledge models as an intermediary layer between raw data and analytics. These models capture the connection between domain knowledge and data, enabling more accurate and meaningful analysis while maintaining manageable complexity through structured knowledge representation.
Solution Approach 2:
The patent performs preliminary organization of domain knowledge into structured models before data analysis. This pre-processing of knowledge structures enables more accurate analytics by establishing the connection between domain knowledge and data in advance, rather than attempting to establish it during the analysis process.
2Productivity
If data preprocessing is performed manually or with traditional methods, then the process is simple, but it is time-consuming and cannot handle growing amounts of data efficiently
Solution Approach 1:
The patent implements automated data preprocessing systems that perform preprocessing tasks without significant human intervention. The system automatically organizes data, applies domain knowledge models, and performs preprocessing operations, enabling efficient handling of growing data volumes while reducing the time loss associated with manual preprocessing.
3Reliability
If commercial or research tools are used without data preprocessing facilities, then the tools are ready-to-use, but they cannot intelligently analyze data due to real world data problems
Solution Approach 1:
The patent merges data preprocessing facilities with commercial and research tools into an integrated system. This combination enables the tools to handle real-world data problems while maintaining reliability, as the preprocessing component prepares data appropriately before analysis without requiring separate manual preprocessing steps.
4Adaptability or versatility
If algorithms are designed optimized only for a certain performance metric without domain dependent constraints, then the design is straightforward, but the solution may be completely different from what is needed for the specific application
Solution Approach 1:
The patent applies domain-dependent bounding constraints to algorithms based on specific application requirements. This enables algorithms to be adapted to local application needs rather than using a one-size-fits-all approach, improving application adaptability while managing complexity through targeted constraint application.
Data Source
AI summary
This disclosure relates generally to method for signal pre-processing based on a plurality of data driven models and a data dependent model transformation. The method includes (a) receiving, a raw signal as an input; (b) learning, a set of representational basis from the received raw signal, wherein the set of representational basis comprises a plurality of orthonormal vectors; (c) selecting, at least one orthonormal vector from the plurality of orthonormal vectors, (d) determining, a structure of the plurality of dictionary atoms, wherein structure of the plurality of dictionary atoms corresponds to a graph structure represented as a Laplacian matrix (L); (e) integrating, the graph structure as a structure of the set of representational basis to obtain a reconfigured data model; and (f) reconstructing, using the reconfigured data model to obtain a denoised signal, wherein at least one of constraints on a optimization problem corresponds to desired spectral and topological structure.