Autoencoder Control Using Latent-Space Sensor Prediction
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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, allowing for the computation of forward predictions in a latent space, thereby facilitating more accurate and efficient control by automatically selecting optimal parameters and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual parameter selection in parameterized models is used, then control system implementation is straightforward, but redundancy and computational complexity increase
Solution Approach 1:
The autoencoder automatically selects optimal parameters from sensor data without manual intervention. The system performs self-service by learning the mapping between sensor data and control parameters through unsupervised training, eliminating the need for manual parameter selection while reducing computational redundancy through efficient latent space representation.
2Ease of manufacture
If manual parameter selection is used, then system implementation is simple, but prediction accuracy decreases
Solution Approach 1:
The autoencoder automatically selects optimal parameters from sensor data without manual intervention. The system performs self-service by learning the mapping between sensor data and control parameters through unsupervised training, eliminating the need for manual parameter selection while reducing computational redundancy through efficient latent space representation.
Solution Approach 2:
The system transforms the parameter selection problem from manual fixed parameters to dynamic learned parameters. The autoencoder learns optimal parameter values by minimizing reconstruction error during training, allowing the parameters to adapt to the specific characteristics of the sensor data and control task, thereby improving prediction accuracy.
3Productivity
If traditional sensor data processing is used, then computational resources are consumed, but prediction accuracy is limited
Solution Approach 1:
The autoencoder extracts the essential features and relationships from sensor data by encoding them into a latent space representation. This extraction process separates the critical information needed for accurate predictions from redundant data, enabling more efficient processing while maintaining or improving prediction accuracy through focused computation on the most relevant features.
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.