Synthetic Sensor Data Generation Using Shared Decoder Modules
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Solution Overview
Problem
Existing methods require training a separate machine-learning model for each specific sensor generation, which is inefficient and time-consuming, especially for new or pre-development sensor generations with limited data.
Innovation Solution
A method involving an encoder module to compress sensor data into a latent space, using a common decoder module and a sensor generation-specific decoder module to generate synthetic sensor data, allowing for training and emulation across different sensor types and generations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate machine-learning model is trained for each specific sensor generation, then the synthetic sensor data accuracy is improved, but the training time and computational resources increase significantly
Solution Approach 1:
The machine-learning model is segmented into a common decoder module (shared across all sensor generations) and generation-specific decoder modules (tailored to individual sensor generations). This segmentation allows the system to maintain accuracy for specific sensor generations while reducing overall training time through shared feature extraction and common processing logic.
Solution Approach 2:
The common decoder module serves multiple sensor generations simultaneously, performing universal feature extraction and processing that applies across different sensor types. This multi-functionality reduces redundant training efforts while maintaining the capability to generate accurate synthetic data for specific sensor generations when needed.
2Productivity
If a common machine-learning model is used for all sensor generations, then the training efficiency is improved, but the accuracy for specific sensor generations deteriorates
Solution Approach 1:
The model architecture is divided into shared common decoder components and specialized generation-specific decoder components. This segmentation enables efficient common processing for all sensor generations while preserving the ability to generate highly accurate synthetic data for specific generations through their dedicated modules.
Solution Approach 2:
Different parts of the decoder have different levels of specialization: the common decoder module handles general feature extraction efficiently, while the generation-specific decoder modules provide localized optimization for particular sensor generations. This local quality ensures high accuracy for specific generations without sacrificing overall training efficiency.
3Adaptability or versatility
If multiple decoder modules are maintained for different sensor generations, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The decoder is segmented into a common module and generation-specific modules, organizing complexity in a structured manner. This segmentation improves adaptability to different sensor generations while managing device complexity through modular design, where the common module handles shared functionality and specific modules handle generation-specific requirements.
Solution Approach 2:
The common decoder module provides universal functionality that works across all sensor generations, reducing the need for completely separate models for each generation. This multi-functionality maintains high adaptability while reducing overall system complexity by sharing common processing logic.
Data Source
AI summary
A method for generating synthetic sensor data of a specific sensor generation includes (i) providing sensor data, wherein the sensor data results from a detection of at least one sensor of a first sensor type, (ii) compressing the sensor data using an encoder module in order to generate a compressed image of the sensor data, and (iii) generating the synthetic sensor data based on at least one characteristic of a second sensor type, at least one characteristic of the specific sensor generation, and the compressed image of the sensor data using a common decoder module and a specific decoder module for the specific sensor generation. A computer program, an apparatus, and a storage medium for this purpose is also disclosed.