Wall Diagnostic Training Data via AI Sensor Classification
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Solution Overview
Problem
Existing methods for generating training datasets for wall diagnostic devices are inadequate, lacking effective means to classify and recognize objects within walls accurately.
Innovation Solution
A method involving a classifier module that uses artificial intelligence to classify unclassified sensor data, including radar data, to determine object position and type, and generate a training dataset for improved object recognition and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate training datasets for wall diagnostic devices, then the process is simple and quick, but the object recognition accuracy and classification reliability are insufficient
Solution Approach 1:
The patent applies preliminary action by using a pre-trained classifier module (artificial intelligence model) to automatically classify sensor data before it becomes training data. The classifier module is trained in advance on labeled sensor data to recognize objects in walls, and this pre-trained capability is then used to generate new training datasets without requiring manual annotation of each new dataset, thus improving accuracy while maintaining efficiency
Solution Approach 2:
The patent uses copying by generating synthetic training data through the classifier module. The classifier processes real sensor data and produces classified versions that can be copied and added to the training dataset multiple times, creating augmented training data that improves model accuracy without requiring proportional increases in manual data collection and annotation efforts
2Productivity
If manual classification methods are used for sensor data, then the training dataset can be generated, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent applies self-service by implementing an automated classification system where the classifier module performs object recognition and classification tasks autonomously. The system takes sensor data as input and automatically generates classified training data without human intervention in the classification process, making the system serve itself by eliminating manual labor while maintaining high productivity
Solution Approach 2:
The patent replaces mechanical (manual) classification methods with an artificial intelligence-based classifier module. Instead of human operators manually analyzing and classifying sensor data, the system uses machine learning models to automatically process sensor data and generate classified training datasets, dramatically reducing time consumption and labor requirements
Data Source
AI summary
A computer-implemented method for generating a training dataset for training an artificial intelligence for operating a measuring device, in particular a wall diagnostic device, includes (i) receiving unclassified sensor data of at least one sensor unit of a measuring device by a classifier module, (ii) classifying the unclassified sensor data and providing classified sensor data by running the unclassified sensor data through the classifier module, and (iii) adding the classified sensor data to a training dataset.