Abstract: Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.

Abstract: Systems and methods herein address power for one or more processing units, using one of a plurality of power profiles during execution of a group of real-time instructions, the one of the plurality of power profiles determined based in part on a relationship determined between the one of the plurality of power profiles and a power profile of the group of real-time instructions, the relationship limited by a threshold, and the plurality of power profiles are associated with a plurality of groups of reference instructions.

Abstract: A method includes determining, using a processing device, a set of observations from coolant data, the coolant data being received from one or more sensors in an environment associated with a coolant. The method further includes determining, using a machine learning model and the set of observations, a contamination level of the coolant. The method also includes initiating an operation, using the processing device, responsive to determining the coolant contamination level.

Abstract: A virtual representation of a physical environment can be generated through simulation, which can include one or more virtual agents to represent robots, or at least semi-automated devices, that can operate and perform various tasks in the physical environment. Various component failures, or other potential problems, can be simulated that can be analyzed by one or more deep learning models associated with the virtual agents. These deep learning models can attempt to diagnose the simulated problem, as well as determine one or more potential solutions. The virtual agents can help to gather information for these determinations, as well as to perform tasks for these potential solutions. Once these deep learning models are trained in this simulated environment, these models can be used by one or more robots to perform tasks that may relate to maintenance or operation of a physical environment.

Abstract: A robot device determines an error associated with equipment included in a data center environment. The robot device may compare the error to candidate errors for which the robot device is already trained to resolve. Based on a result of the comparison, the robot device may perform, in a control environment, candidate maintenance operations in association with resolving the error. The robot device may learn a set of actions associated with successfully resolving the error, based on performing the candidate maintenance operations. The robot device may perform maintenance operations associated with the error. Performing the maintenance operations may include applying the learned set of actions.

Abstract: Methods, devices, and systems automatically monitor and mitigate contamination caused by particulate in air that enters a server enclosure during operation. A particulate sensor detects an amount of particulate in the air and, when the amount exceeds a predetermined particulate limit value, a filter is automatically moved into an airflow path between an environment outside of the server enclosure and an environment inside the server enclosure. In this position, air entering the server enclosure is caused to pass through the filter when the predetermined particulate limit value is exceeded. In addition to active filtering, detected contamination across several units of scale is mapped to generate an air quality trend. The air quality trend provides information to technical personnel about contamination events and can aid in addressing the source of contamination before damage occurs to a server.

Abstract: A tilt sensor includes a one-way rotational drive configured to translate rotational motion of an input shaft in a first rotational direction about a first axis of the input shaft and in a second rotational direction about the first axis into rotational movement in a first rotational direction about a second axis of an output shaft. The tilt sensor further includes a pendulum coupled to the input shaft. The pendulum is configured to rotate the input shaft in the first rotational direction or the second rotational direction responsive to a tilt motion of the tilt sensor. The tilt sensor further includes a motion meter coupled to the output shaft. The motion meter is configured to display a measure of total tilt motion of the tilt sensor.

Abstract: Systems and methods for datacenter are disclosed. In at least one embodiment, a system or method herein causes a process for a structure that includes a component tracking system, where such a system includes individual components within individual servers or individual racks, the process being based in part on at least location information and configuration information from one or more of an optical sensor or a radio sensor associated with a motile-support that is adapted for at least three dimensional (3D) movement in a space having the individual servers or the individual racks.