Abstract: A method of creating automatically a merged video stream combining live-action and animation, comprising (1) receiving a video stream comprising a plurality of frames depicting a live-action scene in which one or more persons participates in an activity, the video stream captured by one or more video cameras is associated with an annotated timecode associating a timestamp of one or more of the plurality of frames depicting one or more of a plurality of predefined events relating to the activity with a description of the predefined events, (2) selecting automatically one or more of a plurality of predefined animation elements according to one or more event attributes of the predefined events, (3) creating a merged video stream by merging the selected predefined animation elements into the one or more frames identified by their timestamps, and (4) outputting an editable digital record of the merged video stream.

Abstract: A method of applying feedback control on peripheral units supplying energy to manufacturing units in a manufacturing facility uses a trained model, and includes: mapping supply relations between peripheral units and manufacturing units into a schema; establishing communications with sensors monitoring peripheral unit metrics indicative of energy transfer from the peripheral units to the manufacturing units; training the model based on the schema and training data gathered by communication with the sensors during a training period, the trained model predicting energy usage by the peripheral machines with a specified degree of accuracy; and during a control period following the training period, gathering further data from the sensors and minimizing energy usage by the peripheral units while supplying the total energy demanded by the manufacturing units by controlling at least one of the peripheral units based on an outcome of inputting the further data into the trained model.

Abstract: A method of applying feedback control on peripheral units supplying energy to manufacturing units in a manufacturing facility uses a trained model, and includes: mapping supply relations between peripheral units and manufacturing units into a schema; establishing communications with sensors monitoring peripheral unit metrics indicative of energy transfer from the peripheral units to the manufacturing units; training the model based on the schema and training data gathered by communication with the sensors during a training period, the trained model predicting energy usage by the peripheral machines with a specified degree of accuracy; and during a control period following the training period, gathering further data from the sensors and minimizing energy usage by the peripheral units while supplying the total energy demanded by the manufacturing units by controlling at least one of the peripheral units based on an outcome of inputting the further data into the trained model.

Abstract: A method of applying feedback control on peripheral units supplying energy to manufacturing units in a manufacturing facility uses a trained model, and includes: mapping supply relations between peripheral units and manufacturing units into a schema; establishing communications with sensors monitoring peripheral unit metrics indicative of energy transfer from the peripheral units to the manufacturing units; training the model based on the schema and training data gathered by communication with the sensors during a training period, the trained model predicting energy usage by the peripheral machines with a specified degree of accuracy; and during a control period following the training period, gathering further data from the sensors and minimizing energy usage by the peripheral units while supplying the total energy demanded by the manufacturing units by controlling at least one of the peripheral units based on an outcome of inputting the further data into the trained model.

Abstract: A method of applying feedback control on peripheral units supplying energy to manufacturing units in a manufacturing facility uses a trained model, and includes: mapping supply relations between peripheral units and manufacturing units into a schema; establishing communications with sensors monitoring peripheral unit metrics indicative of energy transfer from the peripheral units to the manufacturing units; training the model based on the schema and training data gathered by communication with the sensors during a training period, the trained model predicting energy usage by the peripheral machines with a specified degree of accuracy; and during a control period following the training period, gathering further data from the sensors and minimizing energy usage by the peripheral units while supplying the total energy demanded by the manufacturing units by controlling at least one of the peripheral units based on an outcome of inputting the further data into the trained model.