Abstract: An example may be found in a method of identifying one or more cooling units of a plurality of cooling units servicing a data center that has a reduced relative performance level relative to other of the plurality of cooling units. The method includes monitoring a performance level of each of the plurality of cooling units and comparing the performance level to that of neighboring cooling units. In response to determining that at least one of the respective cooling units has a reduced relative performance level, an alert is issued.

Abstract: Using artificial intelligence, arbitrary synthetic three dimensional weather data may be generated using descriptive information about at least one weather element. A weather element is a type of weather such as rain, wind, cloud, and any other type of weather regardless of complexity; descriptive information of a weather element may include weather type and/or characteristics of the weather type (e.g., relative position with respect to a body, dimensions, shape, intensity, and or any other characteristic of the weather type). Such descriptive information of a weather element may be provided as text, image(s), and/or any other form of descriptive information. Optionally, such synthetic three dimensional weather data may be received by a two dimensional weather algorithm to ascertain whether the algorithm properly processes such data into a two dimensional image. Thus, the two dimensional weather algorithm may be evaluated over a more diverse range of weather conditions to ensure its accuracy.

Abstract: A system comprises a set of databases in communication with a processor, and a requirements validation AI engine hosted by the processor. The databases comprise software requirements, regulatory guidelines, historical requirements defects, requirements review checklist, and prompts. The AI engine communicates with the databases, and a fine-tuned LLM communicates with the AI engine. A user interface communicates with the AI engine, and a configuration management repository communicates with the user interface. The user interface sends new requirements, from the configuration management repository, with corresponding check instructions to the AI engine, which selects prompts for review, based on types of new requirements and the check instructions. The AI engine sends the prompts and the new requirements to the LLM to review for any defects in the new requirements. The LLM sends a response to the user interface, to report on validity of and any defects in the new requirements.

Abstract: Techniques are generating a test plan, based on test requirements, using artificial intelligence (AI) whilst avoiding hallucinations. Hallucinations in the AI generated test plan may be reduced by utilizing generative AI and providing the generative AI with pre-defined custom templates, test plan guidelines, and/or information for selecting a test environment based on test type. Additionally or alternatively, a similarity between the test plan and a similar test plan may be determined to avoid hallucinations.

Abstract: 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.

Abstract: A system includes processor(s) configured to: receive natural language text describing software requirements for software program; analyze natural language text describing software requirements to identify relationships between different software requirements at least in part by: analyzing how data flows between different software requirements; analyzing how different software requirements influence path and decision points to achieve functionality identified by software requirements; and identifying dependencies between different software requirements; establish sequence for different software requirements based on relationships identified between different software requirements; group plurality of different software requirements together into logical group(s) of software requirements based on sequence for different software requirements and relationships between different software requirements; generate test cases based on logical group(s) of software requirements; execute software program using test cases;

Abstract: 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.

Abstract: An example may be found in a method of identifying one or more cooling units of a plurality of cooling units servicing a data center that has a reduced relative performance level relative to other of the plurality of cooling units. The method includes monitoring a performance level of each of the plurality of cooling units and comparing the performance level to that of neighboring cooling units. In response to determining that at least one of the respective cooling units has a reduced relative performance level, an alert is issued.

Abstract: One or more environmental conditions within each of a plurality of server racks are received over time. One or more IT parameters representative of server load are received over time. A model is constructed that models how one or more of the environmental conditions within at least one of the server racks of the plurality of server racks responds to changes in one or more of the IT parameters. A future value of one or more environmental conditions within one or more of the plurality of server racks is predicted based at least in part on the model and the one or more subsequent received IT parameters. At least some of the environmental control equipment of the data center is proactively controlled based at least in part on the predicted future value of one or more of the environmental conditions within the one or more server racks.

Abstract: During a deployment phase, each worker that enters a secure area is identified. The behavior of each worker is monitored while the worker is in the secure area in order to develop an Artificial Intelligence (AI) model that is specific to each worker and is representative of at least part of the behavior of the corresponding worker in the secure area. During an operational phase, each worker that enters the secure area is identified. The behavior of each worker is monitored while the worker is in the secure area. The current behavior of each worker in the secure area is compared with the behavior represented by the AI model that corresponds to the worker. An alarm is raised when the current behavior of one or more workers diverges from the behavior represented by the AI model that corresponds to the worker.

Abstract: One or more environmental conditions within each of a plurality of server racks are received over time. One or more IT parameters representative of server load are received over time. A model is constructed that models how one or more of the environmental conditions within at least one of the server racks of the plurality of server racks responds to changes in one or more of the IT parameters. A future value of one or more environmental conditions within one or more of the plurality of server racks is predicted based at least in part on the model and the one or more subsequent received IT parameters. At least some of the environmental control equipment of the data center is proactively controlled based at least in part on the predicted future value of one or more of the environmental conditions within the one or more server racks.

Abstract: 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.

Abstract: A data center includes a plurality of tenants, with some of the server racks within the data center specifically assigned to each of the tenants. A monitoring system may be configured to generate unique tenant dashboards that allow each tenant to view a variety of different information pertaining to the server racks that are assigned to them without allowing other tenants to view their dashboards. The system is configured to generate the unique tenant view for each tenant based on tenant profile information collected by the data center as part of a tenant onboarding process and input into the first and second tenant profiles. The system reads the various parameters in the first and second tenant profiles and automatically generates the unique tenant dashboards for each tenant without having to make programming changes to other parts of the system.

Abstract: An example may be found in a method of identifying one or more cooling units of a plurality of cooling units servicing a data center that has a reduced relative performance level relative to other of the plurality of cooling units. The method includes monitoring a performance level of each of the plurality of cooling units and comparing the performance level to that of neighboring cooling units. In response to determining that at least one of the respective cooling units has a reduced relative performance level, an alert is issued.

Abstract: One or more environmental conditions within each of a plurality of server racks are received over time. One or more IT parameters representative of server load are received over time. A model is constructed that models how one or more of the environmental conditions within at least one of the server racks of the plurality of server racks responds to changes in one or more of the IT parameters. A future value of one or more environmental conditions within one or more of the plurality of server racks is predicted based at least in part on the model and the one or more subsequent received IT parameters. At least some of the environmental control equipment of the data center is proactively controlled based at least in part on the predicted future value of one or more of the environmental conditions within the one or more server racks.

Abstract: 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.

Abstract: A data center includes a plurality of tenants, with some of the server racks within the data center specifically assigned to each of the tenants. A monitoring system may be configured to generate unique tenant dashboards that allow each tenant to view a variety of different information pertaining to the server racks that are assigned to them without allowing other tenants to view their dashboards. The system is configured to generate the unique tenant view for each tenant based on tenant profile information collected by the data center as part of a tenant onboarding process and input into the first and second tenant profiles. The system reads the various parameters in the first and second tenant profiles and automatically generates the unique tenant dashboards for each tenant without having to make programming changes to other parts of the system.

Abstract: During a deployment phase, each worker that enters a secure area is identified. The behavior of each worker is monitored while the worker is in the secure area in order to develop an Artificial Intelligence (AI) model that is specific to each worker and is representative of at least part of the behavior of the corresponding worker in the secure area. During an operational phase, each worker that enters the secure area is identified. The behavior of each worker is monitored while the worker is in the secure area. The current behavior of each worker in the secure area is compared with the behavior represented by the AI model that corresponds to the worker. An alarm is raised when the current behavior of one or more workers diverges from the behavior represented by the AI model that corresponds to the worker.

Abstract: 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.