Underground Detection Device With Machine Learning Signal Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing detection methods for underground objects are limited in their ability to provide detailed and reliable non-destructive detection results, often requiring complex adjustments and user intervention, and struggle with variability in detection accuracy due to different collection situations.
Innovation Solution
A detection device utilizing machine learning processes to evaluate detection signals, allowing for automated adjustment and simplified operation by dividing example values into groups based on cluster formation and using nearest neighbor classification, with a dataset comprising user data for robust model creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to detect underground objects, then the detection device can operate without machine learning processes, but the detection accuracy and reliability are limited and require complex manual adjustments
Solution Approach 1:
The detection device automatically adapts to different collection situations through machine learning processes. The system self-adjusts by processing example values, performing cluster formation, and selecting appropriate evaluation parameters without requiring manual intervention or complex user adjustments, thereby improving detection accuracy while maintaining operational simplicity
Solution Approach 2:
The system performs preliminary processing of example values through cluster formation and nearest neighbor classification before actual detection. By pre-grouping example values and pre-selecting evaluation parameters based on similarity, the system prepares optimal detection settings in advance, eliminating the need for complex real-time adjustments during operation
2Reliability
If traditional detection methods are used, then the operation procedure is simple without machine learning, but the detection results lack detail and reliability
Solution Approach 1:
The system uses feedback from example values and cluster formation results to continuously improve detection reliability. By comparing new detection signals with grouped example values and selecting the most appropriate evaluation parameters based on similarity, the system automatically refines its detection accuracy while requiring minimal user interaction
Solution Approach 2:
The patent replaces manual adjustment mechanisms with automated machine learning processes. Instead of requiring users to manually configure detection parameters, the system uses computational algorithms including cluster formation, nearest neighbor classification, and automatic parameter selection to achieve reliable detection results with minimal user involvement
3Measurement precision
If manual evaluation parameter assignment is used for each example value, then the model can be trained with detailed parameters, but the teach-in phase becomes time-consuming and complex
Solution Approach 1:
The system segments the large set of example values into smaller clusters based on similarity through cluster formation. By grouping example values with comparable characteristics, the system reduces the number of individual evaluations needed during training, significantly reducing the teach-in phase duration while maintaining comprehensive model coverage
Solution Approach 2:
The system creates representative copies of evaluation parameters from similar example values within the same cluster. Instead of manually assigning parameters to each individual example value, the system copies parameters from representative examples in each cluster, dramatically reducing the time required for model training while preserving training precision
Data Source
AI summary
A method for operating a detection device includes providing a detection device configured to non-destructively acquire a detection signal from an object arranged within an underground examination region. The method further includes assigning the detection signal to a detection result based on a machine learning process, in order to output the detection result and/or to use this to adjust the detection device.