According to an embodiment, an automaton deforming device includes a transforming unit and a deforming unit. The transforming unit generates second values by transforming first values, which either represent weights assigned to transitions in a weighted finite state automaton or represent values that are transformed into weights assigned to transitions in a weighted finite state automaton, in such a way that number of elements of a set of the first values are reduced and an order of the first values is preserved. The deforming unit deforms a weighted finite state automaton in which weights according to the second values are assigned to transitions.

The invention provides systems and methods for generating an adaptive nonlinear controller and utilizing the adaptive nonlinear controller to regulate the operation of nonlinear process systems. In particular, a method is provided for generating a control model by defining an objective function utilizing a target function that specifies the desired response of the system and a state-space model representing the dynamics of the non-linear system. When executed by a controller the control model causes the regulated system to operate as specified by the target function and thereby produce a product that is consistent with various prescribed quality metrics.

A motion controller has an axis control part and a communication control part. The motion controller includes: a control function storage part that stores a control function received from a higher-level control device by the communication control part; a control function execution part that executes the control function regarding at least one of information of a motor and information of a sensor as an input; and a control information changing part that changes the motor control information based on a result of execution of the control function stored in the control function storage part, the execution of the control function being performed by the control function execution part. The motor control information changed by the control information changing part serves as an input to the axis control part.

The invention discloses a context awareness control device, system and method. The context awareness control device comprises a connection module, a sensor module, an emission control module, an information processing module and an adjustment control module. The sensor module collects environmental information and transmits the environmental information to the information processing module. The information processing module collects use information of a terminal device through the connection module. The adjustment control module makes a comparison between the function situation information and the pre-stored better environment value according to the detected currently executing function, outputs a first environment control signal according to the comparison results to adjust the working state of the terminal device and emits a second environment control signal through the emission control module to adjust the working state of the electrical equipment.

According to an embodiment, a conversion device converts a first automaton into a second automaton, which both are weighted finite state automatons. The first automaton has a boundary of a path assigned with an input symbol, an appearance position of the boundary, and identifiers for identifying paths. The second automaton has path(s) except unnecessary path(s). The device includes a specifying unit and a search unit. The specifying unit is configured to specify, as a start position, a state of the head of a retrieved path in which a combined weight, which is obtained by adding an accumulated weight from an initial state to the state of the head of the retrieved path in the first automaton and a weight of the best path from the state of the head of the retrieved path to a final state, is best. The search unit is configured to search for a path in which a weight from the start position to a final state in the first automaton is best until reaching next boundary.

A motion controller of the present invention has an axis control part that controls an amplifier based on motor control information and a communication control part that receives a motor control command by communication with a higher-level control device, the motor control command serving as a source of the motor control information, and the motion controller includes: a control function storage part that stores a control function received from the higher-level control device by the communication control part; a control function execution part that executes the control function regarding at least one of information of a motor and information of a sensor as an input; and a control information changing part that changes the motor control information based on a result of execution of the control function stored in the control function storage part, the execution of the control function being performed by the control function execution part, wherein the motor control information changed by the control information changing part serves as an input to the axis control part.

A method and assembly for configuring and/or programming an industrial automation component, wherein respective properties at runtime are detected and stored in a database for a plurality of possible combinations of the hardware, the operating system and/or the application program, and wherein a model is generated from the database data and/or optimized using a reinforcement learning process, where a reinforcement learning reward function used during the learning or optimization process seeks to provide an accurate prediction of the properties, the properties at runtime are then predicted for intended or possible combinations using the model compared with a specified requirement, and a suitable combination is subsequently ascertained using the comparison, and the industrial automation component is configured or programmed according to the selected combination, such that the real-time behavior is very precisely predicted such that the industrial automation component can be optimally configured or programmed.

The invention provides systems and methods for generating an adaptive nonlinear controller and utilizing the adaptive nonlinear controller to regulate the operation of nonlinear process systems. In particular, a method is provided for generating a control model by defining an objective function utilizing a target function that specifies the desired response of the system and a state-space model representing the dynamics of the non-linear system. When executed by a controller the control model causes the regulated system to operate as specified by the target function and thereby produce a product that is consistent with various prescribed quality metrics.

A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

A method includes identifying at least one of historical data associated with historical substrate lots processed by substrate processing tools in a substrate processing facility or simulated data for simulated substrate lots processed by simulated substrate processing tools. The method further includes generating features from the at least one of the historical data for the historical substrate lots or the simulated data for the simulated substrate lots. The method further includes training a machine learning model with data input comprising the features to generate a trained machine learning model. The trained machine learning model is capable of generating one or more outputs indicative of one or more corrective actions to be performed in the substrate processing facility.