A system and method for monitoring performance of an industrial process includes an input port for receiving signals representative of one or more performance parameters generated by the industrial process, a user interface including a display and a controller that is operably coupled with the input port and the user interface. The controller is configured to repeatedly receive signals over time via the input port representative of the one or more performance parameters of the industrial process and to generate a plurality of snapshots, wherein each snapshot includes a graphical representation of the one or more performance parameters of the industrial process at a corresponding time. The controller is configured to generate an animatable heat map including two or more of the plurality of snapshots arranged temporally and to display the animatable heat map on the display.

A display device (10) and an image display method therefor, an image display system, and a storage medium, which relate to the technical field of display. A controller (101) of the display device (10) can adjust a gamma parameter when the temperature of a display module (102) is high, so as to reduce a drive voltage required by the display module (102) while keeping a brightness change of the display module (102) small. In this manner, the temperature of the display module (102) can be reduced, the display module (102) can be prevented from being damaged, and the display device (10) has a high reliability.

The present application provides a scenario temperature planning method and apparatus, a computer device, and a medium, where the scenario temperature planning method includes: obtaining original data in a current temperature planning cycle in a target scenario, where the original data is data generated based on a user's temperature adjustment operation; performing data merging and data elimination processing on the original data to obtain valid data; and adjusting temperature planning data of the current temperature planning cycle based on the valid data to obtain temperature planning data of a next temperature planning cycle of the current temperature planning cycle, so as to adjust a temperature in a target scenario in the next temperature planning cycle. The technical solution enables the adjusted temperature to meet the user's temperature requirements without requiring the user to manually plan and set the temperature, thereby improving user experience.

Provided is an electronic device for controlling surface heart and a method of controlling the electronic device. The electronic device includes a speaker, a temperature sensor, a memory, and a processor electrically coupled to the speaker, the temperature sensor, and the memory. The processor obtains first temperature information based on impedance information measured in a coil included in the speaker; obtains second temperature information measured by the temperature sensor, the second temperature information based on a heat source disposed adjacent to the speaker; predicts a surface temperature of a surface area of the electronic device, opposite to an internal area in which the speaker is disposed, based on the first temperature information and the second temperature information using a nonlinear approximation function; and controls an audio signal input to the speaker based on the predicted surface temperature.

A distributed data processing system includes a processing center or algorithm persistence system (“APS”), a series of remote caching nodes in electronic communication with the APS, and a series of remote computing or processing nodes in electronic communication with the remote caching nodes. Each remote caching node is mounted to a top surface of a mobile vehicle and includes a data transmitter/receiver (transceiver), computer hardware and software to operate the caching node, memory to transmit or transfer data from the APS to the remote processing nodes. The remote processing nodes include a series of electricity generating solar panels, a series of electronic data processing chips, electronic data memory, an electronic date transmitter/receiver (transceiver), and a motion sensor. The series of electronic data processing chips are preferably a tensor processing unit (TPU), which is an AI accelerator application-specific integrated circuit (ASIC) developed specifically for neural network machine learning.

The disclosed computing device may include electronic components, at least one of which is a processor. The computing device may also include a heat sink thermally coupled to the electronic components, as well as a temperature sensor that determines the current temperature inside the computing device. The computing device may further include a controller. The processor may generate a load schedule for the electronic components based on the current temperature inside the computing device. This load schedule ensures that a maximum temperature for the heat sink is not exceeded even when the total system power load exceeds, for a short period of time, the maximum sustainable power level the heat sink can dissipate. The controller may then load the electronic components according to the generated load schedule. Various other methods, systems, and computer-readable media are also disclosed.

Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.

Systems and methods are disclosed for scheduling threads on a processor that has at least two different core types, such as an asymmetric multiprocessing system. Each core type can run at a plurality of selectable voltage and frequency scaling (DVFS) states. Threads from a plurality of processes can be grouped into thread groups. Execution metrics are accumulated for threads of a thread group and fed into a plurality of tunable controllers for the thread group. A closed loop performance control (CLPC) system determines a control effort for the thread group and maps the control effort to a recommended core type and DVFS state. A closed loop thermal and power management system can limit the control effort determined by the CLPC for a thread group, and limit the power, core type, and DVFS states for the system. Deferred interrupts can be used to increase performance.

In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center with respect to design and risk aspects.

An information handling system includes a corrosion controller that may monitor a corrosion sensor array, and determine a type of the corrosion based on a location of a corrosion sensor. The corrosion type may include humidity driven corrosion and non-humidity driven corrosion.