Autoencoder Control for Sensor Prediction and Redundancy Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing control systems face inefficiencies in processing sensor data due to manual selection of parameters in parameterized models, leading to redundancy and increased computational complexity, which affects accuracy and resource usage.
Innovation Solution
The implementation of an autoencoder in a control system to process sensor data, where the autoencoder is trained to compute forward predictions in a latent space, facilitating improved parameter selection and reducing redundancy by learning relationships in the data, thereby enhancing prediction accuracy and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter selection in parameterized models is used, then control systems can operate with simpler computational structures, but redundancy increases and computational complexity increases, affecting accuracy and resource usage
Solution Approach 1:
The autoencoder automatically learns optimal parameter selections and relationships from sensor data without manual intervention. The system performs self-training by encoding sensor data into latent representations and decoding them, automatically identifying patterns and relationships that improve prediction accuracy while reducing redundancy in the computational process
Solution Approach 2:
The patent replaces manual parameter selection mechanisms with an automated neural network-based autoencoder system. This substitution transforms the control system from one requiring manual configuration to an autonomous system that learns parameters automatically, reducing both redundancy and computational complexity while improving prediction accuracy
2Productivity
If manual parameter selection is used in control systems, then device structure remains simpler, but resource usage increases due to redundancy and computational complexity
Solution Approach 1:
The autoencoder learns efficient parameter representations automatically from sensor data, optimizing resource usage without manual configuration. By training on historical sensor data and derived values, the system autonomously identifies the most efficient computational paths and parameter selections, improving resource usage efficiency while maintaining manageable computational structure
Solution Approach 2:
The system dynamically adjusts parameters by learning optimal transformations from sensor data through the autoencoder. The latent space representations and learned parameter relationships enable the system to adapt computational parameters automatically, improving resource efficiency by eliminating redundant computations and optimizing the computational structure
Data Source
AI summary
A control system comprises a memory storing a sequence of sensor data received from one or more sensors. The control system has a processor which processes the sensor data to compute a sequence of derived sensor data values. An autoencoder receives the sequence of derived sensor data values and computes a forward prediction of the sequence of derived sensor data values, the autoencoder having been trained imposing a relationship on positions of the derived sensor data values encoded in a latent space of the autoencoder. A processor initiates control of an apparatus using the forward prediction.