Telefonaktiebolaget lm ericsson (publ) (20240137861). POWER SUPPLY CONTROL METHOD simplified abstract
Contents
POWER SUPPLY CONTROL METHOD
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Lackis Eleftheriadis of Valbo (SE)
Athanasios Karapantelakis of Solna (SE)
Konstantinos Vandikas of Solna (SE)
Sunil Kumar Vuppala of Bangalore (IN)
POWER SUPPLY CONTROL METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240137861 titled 'POWER SUPPLY CONTROL METHOD
Simplified Explanation
The abstract describes methods and systems for controlling a power supply unit (PSU) using machine learning. The method involves measuring properties of the PSU, transmitting the measurements to a machine learning agent, processing the measurements with a trained ML model to generate suggested actions, predicting the effects of the actions on the PSU properties, selecting significant actions, transmitting the selected actions to the PSU, and performing the actions at the PSU.
- Measuring properties of the PSU
- Transmitting measurements to a machine learning agent
- Processing measurements with a trained ML model
- Generating suggested actions based on processed measurements
- Predicting effects of actions on PSU properties
- Selecting significant actions
- Transmitting selected actions to the PSU
- Performing selected actions at the PSU
Potential Applications
The technology can be applied in various industries where precise control and optimization of power supply units are required, such as telecommunications, data centers, and industrial automation.
Problems Solved
This technology solves the problem of inefficient manual control of power supply units, leading to suboptimal performance and potential equipment damage. By utilizing machine learning algorithms, the system can automatically adjust PSU settings for optimal operation.
Benefits
The benefits of this technology include improved efficiency, reduced downtime, extended equipment lifespan, and enhanced overall system performance. By leveraging machine learning, the system can adapt to changing conditions in real-time, ensuring optimal PSU operation.
Potential Commercial Applications
The technology can be commercially applied in industries that rely on stable and efficient power supply units, such as telecommunications infrastructure providers, cloud computing companies, and manufacturing facilities.
Possible Prior Art
One possible prior art in this field is the use of traditional control systems and algorithms to manage power supply units. However, the integration of machine learning for real-time optimization and decision-making represents a novel approach to PSU control.
Original Abstract Submitted
methods and systems for power supply unit (psu) control. a method includes measuring one or more properties of the psu to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ml) agent hosting a trained ml model. the method further includes receiving the property measurements at the ml agent, and processing the received property measurements using the trained ml model to generate suggested actions to be taken by the psu. the method further includes predicting the effect of each of the suggested actions on the measured psu properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured psu properties. the method further includes initiating transmission of the selected subset of suggested actions to the psu, and performing, at the psu, the selected subset of suggested actions.
