Sensor Data Weighting for Faster Classification With Less Processing
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Solution Overview
Problem
Existing methods for processing sensor data are inefficient and require processing large amounts of data, which can be time-consuming and resource-intensive.
Innovation Solution
The method involves dividing sensor data into parts, mapping each part onto a representation (such as a tensor), assigning weights based on heuristic knowledge, and using these weights to reduce the data processed, incorporating Fourier coefficients and principal component analysis to characterize information content, and employing an artificial neural network for classification or regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all parts of sensor data are processed, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides sensor data into multiple parts or channels, allowing selective processing of only those parts that contain relevant information. This segmentation enables the system to process a subset of data rather than all data, reducing processing time while maintaining classification accuracy through targeted analysis of informative segments.
Solution Approach 2:
The patent assigns different weights to different parts of sensor data based on their information content, treating each part with differentiated quality assessment. By identifying and prioritizing locally informative regions or channels while downweighting or excluding redundant parts, the system achieves accurate classification with reduced processing time.
2Loss of information
If all parts of sensor data are processed, then information completeness is improved, but resource consumption increases
Solution Approach 1:
The patent processes only a partial subset of sensor data parts that are deemed most informative, rather than exhaustively processing all available data. By applying partial action selectively to high-value data segments, the system maintains sufficient information completeness for accurate classification while significantly reducing computational resource consumption.
Solution Approach 2:
The patent transforms sensor data into different representations (such as Fourier coefficients) and uses these transformed parameters to assess information content. By changing the representation parameters and selecting parts with highest information density, the system achieves efficient processing with reduced resource consumption while preserving essential information.
3Productivity
If heuristic knowledge is incorporated into weight assignment, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent enables the system to automatically assess information content of different data parts and assign weights autonomously without requiring complex external control mechanisms. The self-service approach uses intrinsic properties of the data (such as entropy calculations from Fourier coefficients) to drive the weighting process, improving processing efficiency while keeping the added complexity manageable and self-contained.
Data Source
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AI summary
A device and a computer-implemented method for processing sensor data, wherein the sensor data is divided into parts (202) and the parts of the sensor data are each mapped onto a representation, in particular a tensor (204), wherein for each representation a weight assigned to the representation is determined depending on the representation (208), which characterizes an information content of the part of the sensor data represented by the representation, wherein weights are drawn from a distribution of the weights determined for the representations (210), wherein a classification and/or regression of the sensor data is determined depending on the representations assigned to the weights drawn (212).