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.

The present application discloses a wide-field-of-view anti-shake high-dynamic bionic eye. A trajectory tracking method based on a bionic eye robot includes: establishing a linear model according to a bionic eye robot; establishing a full state feedback control system on the basis of the linear model; in the full state feedback control system, acquiring an angle and an angular acceleration required for a joint in a target tracking process of the bionic eye on the basis of a preset trajectory expectation value and a preset joint angle expectation value; the method further includes: adopting a linear quadratic regulator (LQR) to calculate a parameter K in the full state feedback control system, and minimizing energy consumption by establishing an energy function, so as to optimize the coordinated head-eye motion control of the linear bionic eye. The present application achieves the optimal control of the target tracking.

Systems, computer program products, and methods are described herein for decentralized edge computing enablement in robotic process automation. The present invention is configured to receive an indication that a hosted virtual desktop (HVD) has received a first set of instructions for execution from a controller hosted virtual desktop (CHVD); electronically receive, from the HVD, an indication that the first set of instructions have been executed by the HVD; predict, using the edge computing enablement engine, a second task to be executed by the HVD; determine, using the quantum database search algorithm, a location of the second task in the knowledge repository; retrieve a second set of instructions associated with the second task from the location of the second task in the knowledge repository; and receive, from the HVD, an indication that the second set of instructions have been executed by the HVD.

A predictive control system includes controllable equipment and a controller. The controller is configured to use a neural network model to predict values of controlled variables predicted to result from operating the controllable equipment in accordance with corresponding values of manipulated variables, use the values of the controlled variables predicted by the neural network model to evaluate an objective function that defines a control objective as a function of at least the controlled variables, perform a predictive optimization process to generate optimal values of the manipulated variables for a plurality of time steps in an optimization period using the neural network model and the objective function, and operate the controllable equipment by providing the controllable equipment with control signals based on the optimal values of the manipulated variables generated by performing the predictive optimization process.

A parameter compression unit compresses first input parameter values so that a parameter restoration unit can restore the first input parameter values, and generates first compressed input parameter values in which the number of control parameters is reduced, a model learning unit learns a prediction model from learning data that is a set of the first compressed input parameter values and first output parameter values that processing results obtained by giving the first input parameter values, as a plurality of control parameters, to a processing device, and a processing condition search unit estimates a second compressed input parameter values corresponding to target output parameter values by using the prediction model.

There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.

A system for detection of an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing a task. Receive signals including robot, worker and DMP signals. Predict a sequence of events (SOEs) from DMP signals. Determine whether the predicted SOEs in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted SOEs from DMP signals is inconsistent with the behavior, then an alarm is to be signaled. Input worker data into a Human Performance (HP) model, to obtain a state of the worker based on previously learned boundaries of human state. The state of the HW is then input into the HRI model and the DMP model to determine a classification of anomaly or no anomaly. Update a Human-Robot Interaction (HRI) model to obtain a control action of a robot or a type of an anomaly alarm.

A system and method are provided for operating a power generating asset. Accordingly, at least one external data set indicative of a plurality of variables affecting the performance of the power generating asset is received by the controller. The controller also receives at least one operational data set indicative of the performance of the power generating asset. A plurality of production-assessment models for the power generating asset are generated and trained based on the data sets. A performance prediction is then generated for each of a plurality of model-variable combinations and a control action is implemented based on one of the performance predictions.

A method of determining productive capacity parameters includes steps of: obtaining a plurality of parameters of a production line from a memory. By a productive capacity parameters generating system finishing the following steps of: combining parameters of production line to obtain a plurality of parametric combinations so as to generate a plurality of production capacity values; calculating a plurality of stimulation values according to production capacity values and parameters; when at least one stimulation value of stimulation values is greater than to or equals to a preset threshold, setting up at least one stimulation value of stimulation values as at least one related stimulation value; obtaining parameters of at least one target parametric combination of parametric combinations according to at least one related stimulation value; and providing parameters of at least one target parametric combination as productive capacity parameters of production line.

A method and system are provided for improving predictions of electrical power usage. In the method and system, load and/or environmental data is classified into data sets that correspond to different modes of operation of an electrical load. Different predictive models are also provided for each set of classified data. The predictive models may provide more efficient and/or more accurate predictions of power usage since each model is limited to a particular mode of operation.