Operating a substrate processing system includes receiving a plurality of sets of training data, storing a plurality of machine learning models, storing a plurality of physical process models, receiving a selection of a machine learning model from the plurality of machine learning models and a selection of a physical process model from the plurality of physical process models, generating an implemented machine learning model according to the selected machine learning model, calculating a characterizing value for each training spectrum in each set of training data thereby generating a plurality of training characterizing values with each training characterizing value associated with one of the plurality of training spectra, training the implemented machine learning model using the plurality of training characterizing values and plurality of training spectra to generate a trained machine learning model, and passing the trained machine learning model to a control system of the substrate processing system.

A method for training a machine-learning model to assess at least one condition of industrial equipment, and/or at least one condition of a process running in an industrial plant, based on measurement data gathered by a plurality of sensors, includes: obtaining a plurality of records of measurement data that correspond to a variety of operating situations and a variety of conditions; obtaining, for each record of measurement data, a label that represents a condition in the operating situation characterized by the record of measurement data; and determining a plausibility of at least one record of measurement data, and/or a plausibility of at least one label, based at least in part on a comparison with at least one other record of measurement data, with at least one other label, and/or with additional information about the industrial equipment, and/or about the industrial plant where the industrial equipment resides, and/or about the process.

An artificial intelligence (AI) device capable of being controlled according to a user's gaze includes a communication unit, a camera configured to capture an image of a user, and a processor configured to acquire user state information from the image of the user, acquire a gaze position of the user based on the acquired user state information, calculate a distance between the acquired gaze position and the camera, receive, from one or more external AI devices, one or more distances between gaze positions of the user respectively acquired by the external AI devices and cameras respectively provided in the external AI devices through the communication unit, and compare the calculated distance with the received one or more distances to select a controlled device.

A thermal displacement compensation apparatus for compensating a dimensional measurement error due to a thermal displacement of a workpiece, including a machine learning device for learning shape measurement data at the time of inspection of the workpiece, wherein the machine learning device observes image data showing the temperature distribution of the workpiece and shape data after machining as state variables representing the current state of the environment, acquires judgment data indicating the shape measurement data at the time of inspection, and learns the image data showing the temperature distribution of the workpiece and shape data after machining and the shape measurement data at the time of inspection in association with each other using the observed state variables and the acquired judgment data.

Provided is a numerical controller capable of easily controlling a pressure without a pressure sensor. The numerical controller estimates a pressure based on at least one of a command value and a feedback value. A machine learning device for learning the pressure corresponding to the at least one of the command value and the feedback value is included. The machine learning device includes a state observation unit for observing the at least one of the command value and the feedback value as a state variable, a label data acquisition unit for acquiring label data indicating the pressure, and a learning unit for associating and learning the state variable with the label data.

Operating a substrate processing system includes receiving a plurality of sets of training data, storing a plurality of machine learning models, storing a plurality of physical process models, receiving a selection of a machine learning model from the plurality of machine learning models and a selection of a physical process model from the plurality of physical process models, generating an implemented machine learning model according to the selected machine learning model, calculating a characterizing value for each training spectrum in each set of training data thereby generating a plurality of training characterizing values with each training characterizing value associated with one of the plurality of training spectra, training the implemented machine learning model using the plurality of training characterizing values and plurality of training spectra to generate a trained machine learning model, and passing the trained machine learning model to a control system of the substrate processing system.

The present invention discloses a prediction control method and system for component contents in a rare earth extraction process. The prediction control method includes: establishing an Elman neural network model of a rare earth extraction process; obtaining a predicted output value of the rare earth extraction process through the Elman neural network model of the rare earth extraction process; calculating an optimal set value through steady-state optimization; dynamically predicting an extractant flow increment and a detergent flow increment based on the predicted output value and the optimal set value; and controlling component contents in the rare earth extraction process according to the extractant flow increment and the detergent flow increment. According to the present invention, an optimal setting problem of a set point is solved through steady-state optimization calculation, and then an optimal control effect is achieved in combination with a dynamic prediction control method, thereby achieving optimal setting control over the component contents in the rare earth extraction process, and ensuring the product quality of the rare earth extraction process.

Example implementations described herein are directed to a system for manufacturing dispatching using reinforcement learning and transfer learning. The systems and methods described herein can be deployed in factories for manufacturing dispatching for reducing job-due related costs. In particular, example implementations described herein can be used to reduce massive data collection and reduce model training time, which can eventually improve dispatching efficiency and reduce factory cost.

Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Deep learning approaches and systems are described to control the process of casting physical objects. A neural network, operating on one or more processors of a server or distributed computing resources and maintained in one or more data storage devices, is trained to recognize relationships between the target digital representation and the resulting metal parts that are cast, and a number of specific approaches are described herein to overcome technical issues in relation to misalignments between reference points, among others. These deep learning approaches are then used for generation of command or control signals which modify how the casting process is conducted. Command or control signals can be used to modify how a cast mold is made, to modify environmental variables, to modify manufacturing parameters, and combinations thereof.