M2M Data Augmentation Workflow for Larger AI Training Sets
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Solution Overview
Problem
Existing M2M systems face challenges in effectively augmenting data for training artificial intelligence models due to limited data sets, necessitating improved data augmentation techniques to enhance model performance.
Innovation Solution
An apparatus and method for augmenting data in M2M systems by generating and storing augmented data using a transceiver and processor, which includes determining augmentation types and parameters, obtaining original data, and storing the augmented data in a resource associated with data augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data augmentation is performed in M2M systems, then the dataset size for AI model training is increased, but the system complexity and computational resources required are increased
Solution Approach 1:
The M2M system performs data augmentation autonomously using its own computational resources and stored original data, without requiring external intervention or complex external infrastructure. The system self-manages the augmentation process by selecting augmentation types, applying transformations, and storing augmented data back in its database.
Solution Approach 2:
The system creates multiple copies of original data with different transformations applied. Instead of generating entirely new data, it produces augmented versions by copying and transforming existing data, thereby increasing dataset size while maintaining manageable system complexity through reuse of original data assets.
2Reliability
If data augmentation is performed in M2M systems, then the performance of AI applications is improved, but the processing time and computational energy consumption are increased
Solution Approach 1:
The system performs data augmentation in advance and stores the augmented data in the M2M database for future use. By pre-generating augmented datasets before they are needed for AI model training, the system avoids time-consuming augmentation operations during critical training phases, thereby reducing processing time delays.
Solution Approach 2:
The data augmentation process is executed periodically or on-demand based on system conditions, rather than continuously. This allows the system to balance performance improvement with acceptable processing time by performing augmentation at optimal intervals when computational resources are available.
3Reliability
If data augmentation is performed in M2M systems, then the robustness of AI models is enhanced, but the storage requirements and data management complexity are increased
Solution Approach 1:
The system applies different augmentation transformations to different portions or aspects of the original data based on specific requirements. Instead of uniformly augmenting all data with the same transformations, it selectively applies appropriate augmentation types to specific datasets or data characteristics, optimizing storage efficiency while maintaining model robustness.
Data Source
AI summary
The present disclosure relates to augmenting data in a machine-to-machine (M2M) system, and a method for operating an apparatus may include receiving a request message including information necessary for data augmentation, obtaining original data based on the information, generating augmented data from the original data based on the information, and storing the augmented data.