18279795. POWER SUPPLY CONTROL METHOD simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
- 1 POWER SUPPLY CONTROL METHOD
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 18279795 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 selected actions at the PSU.
- The method involves measuring properties of the PSU.
- Property measurements are transmitted to a machine learning agent.
- The agent processes the measurements using a trained ML model to generate suggested actions.
- Predictions are made on the effects of the suggested actions on the PSU properties.
- A subset of significant actions is selected based on the predictions.
- The selected actions are transmitted to the PSU and performed.
Potential Applications
This technology could be applied in various industries where precise control and optimization of power supply units are required, such as data centers, telecommunications, and industrial automation.
Problems Solved
This technology solves the problem of manual control and optimization of power supply units, allowing for more efficient and effective management of PSU operations.
Benefits
The benefits of this technology include improved performance and reliability of power supply units, reduced downtime, energy savings, and enhanced overall system efficiency.
Potential Commercial Applications
Potential commercial applications of this technology include power management systems for data centers, smart grid technologies, and industrial control systems.
Possible Prior Art
One possible prior art in this field is the use of traditional control systems for power supply units, which may lack the advanced capabilities and efficiency offered by machine learning-based control methods.
What are the specific machine learning algorithms used in this method?
The specific machine learning algorithms used in this method are not mentioned in the abstract. Further details on the ML models and algorithms employed would provide a clearer understanding of the technology's implementation.
How does the method ensure the security and privacy of the data transmitted to the machine learning agent?
The abstract does not address the security and privacy measures implemented to protect the data transmitted to the machine learning agent. Ensuring the confidentiality and integrity of the data is crucial in applications involving sensitive information, and additional information on data security protocols would be beneficial.
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.
