Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Methods and systems for data collection in an environment including pumps and fans are disclosed. A monitoring system may include a data collector communicatively coupled to a plurality of input channels, wherein the input channels are communicatively coupled to sensors measuring operational parameters of a pump or fan. A data storage may store one or more frequencies related to an operation of the pump or fan, and a data acquisition circuit may interpret a plurality of detection values from the collected data. A frequency evaluation circuit may detect a signal on one of the input channels at a frequency higher than the one or more frequencies at which the pump or fan operates.

A method is provided for reducing a computational cost of deep reinforcement learning using an input image to provide a filtered output image composed of pixels. The method includes generating a moving gate in which the pixels of the filtered output image to be masked are assigned a first gate value and the pixels of the filtered output image to be passed through are assigned a second gate value. The method further includes applying the input image and the moving gate to a GCNN to provide the filtered output image such that only the pixels of the input image used to compute the pixels assigned the second gate value are processed by the GCNN while bypassing the pixels of the input image useable to compute the pixels assigned the first gate to reduce an overall processing time of the input image in order to provide the filtered output image.

Systems and methods for monitoring and remote balancing a motor are disclosed. An exemplary system may include a plurality analog sensors operationally coupled to a motor, an analog switch communicatively coupled to at least one of analog sensors, wherein a first analog sensor input is a trigger channel and a second is an input channel. The analog switch may digitally derive a relative phase between the trigger channel and the input channel using a phase-lock loop (PLL) band-pass tracking filter on at least one of the analog sensor channels to obtain one of slow-speed rotations per minute (RPMs) or phase information for the motor. A response circuit may recommend a change in a process or an operating parameter of the motor to remotely balance the motor based on one or more of the slow-speed RPMs or the phase information.

Systems and methods for monitoring a pump and a fan are disclosed. A monitoring system for a pump may include an industrial system including a pump, a data acquisition circuit to interpret a plurality of detection values corresponding to at least one of a plurality of input sensors operationally coupled to the pump and communicatively coupled to the data acquisition circuit. A monitoring system may further include a signal evaluation circuit including a timer circuit to generate at least one timing signal and a phase detection circuit structured to determine a relative phase difference between at least one of the detection values and at least one of the timing signals from the timer circuit. A response circuit may perform at least one operation in response to the relative phase difference.

Mechanisms are provided to implement a generative adversarial network (GAN) for natural language processing. With these mechanisms, a generator neural network of the GAN is configured to generate a bag-of-ngrams (BoN) output based on a noise vector input and a discriminator neural network of the GAN is configured to receive a BoN input, where the BoN input is either the BoN output from the generator neural network or a BoN input associated with an actual portion of natural language text. The mechanisms further configure the discriminator neural network of the GAN to output an indication of a probability as to whether the input BoN is from the actual portion of natural language text or is the BoN output of the generator neural network. Moreover, the mechanisms train the generator neural network and discriminator neural network based on a feedback mechanism that compares the output indication from the discriminator neural network to an indicator of whether the input BoN is from the actual portion of natural language text of the BoN output of the generator neural network.

A medical registration training component executing within a medical registration system performs a training medical registration operation on a pair of medical studies. Responsive to the medical registration training system determining that the training medical registration operation succeeds, the medical registration training system records a medical registration instance for the pair of medical studies in a medical registration history and marks the medical registration instance as a positive instance in the medical registration history. Responsive to the medical registration training system determining that the training medical registration operation requires correction, the medical registration training system records a medical registration instance for the pair of medical studies in the medical registration history and marks the medical registration instance as a negative instance in the medical registration history. The medical registration training system trains a failure prediction machine learning model based on the medical registration history using machine learning such that the failure prediction machine learning model predicts whether a new medical registration operation will require correction. Responsive to the failure prediction machine learning model predicting that the new medical registration operation will require correction, the mechanism takes steps to automatically correct the new medical registration operation.

An electronic apparatus for recognizing a user and a method therefor are provided. The electronic apparatus includes a communication interface, a dynamic vision sensor (DVS), a memory including a database in which one or more images are stored, and at least one processor. The at least one processor is configured to generate an image, in which a shape of an object is included, based on an event detected through the DVS, control the memory to store a plurality of images generated under a specified condition, in the database, identify shapes of the user included in each of the plurality of images stored in the database, and generate shape information for recognizing the user based on the identified shapes. The plurality of images may include a shape of a user.

Approaches for automated fashion designing are described. A computer-implemented method for automated fashion designing includes: training, by a computer device, computer models using deep learning based computer vision; identifying, by the computer device, at least one gap using cognitively determined fashionability scores (F-scores); and creating, by the computer device, a new fashion design using the computer models and the at least one identified gap.

An artificial intelligence moving agent is provided. The artificial intelligence moving agent includes: a camera configured to photograph an image, and a processor configured to photograph an object, acquire type information of the object by providing an image of the photographed object to an artificial intelligence model, acquire correction type information designated by a user with respect to the image of the photographed object, and train the artificial intelligence model by using the correction type information.