A computer system provides intelligent context-based control of air flow. An identity of a user and a location of the user within an area that includes one or more vents associated with a climate control system are identified. User preferences are determined based on the identity of the user. Characteristics of air flow in the area are adjusted based on the user preferences the user and the location of the user, wherein the characteristics of the air flow comprise one or more from a group of: a direction, and a flow rate. Embodiments of the present invention further include a method and program product for providing intelligent context-based control of air flow in substantially the same manner described above.

A warm-up evaluation device includes: a temperature data acquisition unit that acquires temperature data before warm-up operation when a machine performs the warm-up operation; a parameter value acquisition unit that acquires parameter values set in a program for performing the warm-up operation; an evaluation data acquisition unit that acquires evaluation data for evaluating a result of the warm-up operation; a learning unit that learns a machine learning model which receives the temperature data and the parameter values as an input and outputs the evaluation data on the basis of a plurality of warm-up operations performed by the same or the same types of machines; and an evaluation unit that inputs candidates for the parameter values to the machine learning model together with the temperature data and outputs the evaluation data when the same or the same types of machines perform a new warm-up operation.

Methods for calibrating metrology apparatuses and determining a parameter of interest are disclosed. In one arrangement, training data is provided that comprises detected representations of scattered radiation detected by each of plural metrology apparatuses. An encoder encodes each detected representation to provide an encoded representation, and a decoder generates a synthetic detected representation from the respective encoded representation. A classifier estimates from which metrology apparatus originates each encoded representation or each synthetic detected representation. The training data is used to simultaneously perform, in an adversarial relationship relative to each other, a first machine learning process involving the encoder or decoder and a second machine learning process involving the classifier.

A method of detecting and classifying anomalies during semiconductor processing includes executing a wafer recipe a semiconductor processing system to process a semiconductor wafer; monitoring sensor outputs from a sensors that monitor conditions associated with the semiconductor processing system; providing the sensor outputs to models trained to identify when the conditions associated with the semiconductor processing system indicate a fault in the semiconductor wafer; receiving an indication of a fault from at least one of the models; and generating a fault output in response to receiving the indication of the fault.

Example implementations described herein are directed to a simulation environment for a real world system involving one or more robots and one or more sensors. Scenarios are loaded into a simulation environment having one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more virtual system to train a control strategy model from reinforcement learning, which is subsequently deployed to the real world environment. In cases of failure of the real world environment, the failures are provided to the simulation environment to generate an updated control strategy model for the real world environment.

An assistive system according to an embodiment of the present disclosure includes a cradle module including a holder body on which a user terminal is to be placed and a drive unit configured to drive the holder body, the cradle module configured to output a terminal connection signal when the user terminal is placed on the holder body, and an assistant server configured to receive the terminal connection signal from the cradle module and execute an assistive service, the assistant server configured to control the drive unit by a preset drive pattern through a drive control signal when executing the assistive service. It is possible to easily and conveniently identify a plurality of assistive services outputted according to each situation by controlling a cradle module through a drive pattern corresponding to an assistive service while outputting a voice corresponding to the assistive service when executing the assistive service.

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

A system for controlling an operation of a machine for performing a task is disclosed. The system submits a sequence of control inputs to the machine and receives a feedback signal. The system further determines, at each control step, a current control input for controlling the machine based on the feedback signal including a current measurement of a current state of the system by applying a control policy transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of a feedback controller. Furthermore, the system may iteratively update a state of the feedback controller defined by the control parameters using a prediction model predicting values of the control parameters and a measurement model updating the predicted values to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.