A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.

Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings. The resulting aligned batch data of the subject industrial process enables improved modeling and control of batch productions by the subject industrial process.

Methods and systems for controlling concentration profiles of deposited films using machine learning are provided. Data associated with a target concentration profile for a film to be deposited on a surface of a substrate during a deposition process for the substrate is provided as input to a trained machine learning model. One or more outputs of the trained machine learning model are obtained. Process recipe data identifying one or more sets of deposition process settings is determined from the one or more outputs. For each set of deposition process setting, an indication of a level of confidence that a respective set of deposition process settings corresponds to the target concentration profile for the film to be deposited on the substrate is also determined. In response to an identification of the respective set of deposition process settings with a level of confidence that satisfies a level of confidence criterion, one or more operations of the deposition process are performed in accordance with the respective set of deposition process settings.

Responding to grid events is provided. The system determines, based on an event, to modify an electrical load of a site. The system selects a parameter for the site to adjust to modify the electrical load. The system identifies a script constructed from previously processed interactions between a human-machine interface of the building management system to adjust the parameter for the site. The system establishes a communication session with a remote access agent executed by a computing device of the site to invoke the building management system of the site. The system generates a sequence of commands defined by the script to adjust the one or more parameters for the site. The system transmits the sequence of commands to cause the remote access agent to execute the sequence of commands on the human-machine interface of the building management system to modify the electrical load of the site.

A high current contact is disclosed having a contact pin for insertion into the high-current socket having a plurality of contact segments that are slotted in a radial direction for contacting an inner contact surface of the high-current socket; a guide sleeve surrounding the contact pin, which, by means of an at least central front pressing against the high-current socket relative to the contact pin in an axial direction from an initial position, in which the guide sleeve blocks an independent radial spreading of the contact segments in order to avoid a contact between the contacts segments protruding axially from the guide sleeve and the inner contact surface, is movable into a contact position that is set back with respect to the contact pin and in which the guide sleeve unblocks an independent radial spreading of the contact segments protruding from the guide sleeve for contacting the inner contact surface.

An indoor air quality control system may be implemented to control a plurality of air handling units within an industrial facility in a concerted effort to effect an overall air quality goal. A remote server analyzes sensor data, historical data, and other environmental data (e.g., predicted weather data), and uses one or more machine learning algorithms to model the behavior of air within the facility. The sensed air quality data is considered holistically to understand the overall condition of the facility and the gradient of air flows and/or contaminant flows within the 3-dimensional space. Air handling models are applied to current sensor data to generate instructions to selectively turn on/off or otherwise control components of various air handling equipment to reach an optimized air quality result. Decisions on how to control the facility are based on environmental health and safety considerations.

A web services platform includes a data collector and a timeseries service. The data collector is configured to collect feedback samples provided by one or more sensors of a building management system and generate one or more feedback timeseries including a plurality of the feedback samples. The timeseries service is configured to identify a feedback control workflow that uses the feedback timeseries as an input and defines one or more processing operations to be applied to the feedback samples of the feedback timeseries, perform the one or more processing operations defined by the feedback control workflow to generate a control signal timeseries including a set of control signal samples, and provide a control signal including at least one of the control signal samples or the control signal timeseries as an output to controllable building equipment of the building management system that operate using the control signal as an input.

A method includes obtaining operating data associated with operation of a cross-directional industrial process controlled by at least one model-based process controller. The method also includes, during a training period, performing closed-loop model identification with a first portion of the operating data to identify multiple sets of first spatial and temporal models. The method further includes identifying clusters associated with parameter values of the first spatial and temporal models. The method also includes, during a testing period, performing closed-loop model identification with a second portion of the operating data to identify second spatial and temporal models. The method further includes determining whether at least one parameter value of at least one of the second spatial and temporal models falls outside at least one of the clusters. In addition, the method includes, in response to such a determination, detecting that a mismatch exists between actual and modeled behaviors of the industrial process.

Provided is a method for configuring a thermal energy storage system including the following steps:

providing a thermal energy storage device for storing heat,

providing a plurality of temperature sensors at different locations of the thermal energy storage device for measuring temperatures at the different locations,

providing a control device of the thermal energy storage system for reading measurement data of the plurality of temperature sensors,

generating a numerical model for at least one first temperature sensor of the plurality of temperature sensors based on the measured temperatures of the plurality of temperature sensors means of machine learning, and

storing the numerical model by a control device, for configuring the thermal energy storage system,

Furthermore, a thermal energy storage system and a method for operating a thermal energy storage system is also provided.