Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points operationally coupled to an industrial chemical process; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and an expert system detection circuit structured to detect a process indicator in response to the plurality of detection values, and to initiate a self-organizing data collection response as a result of the process indicator detection.

A self-learning robot, according to one embodiment of the present invention, comprises: a data receiving unit for sensing video data or audio data relating to an object located within a predetermined range; a data recognition unit for matching data received from the data receiving unit and data included in a database in the self-learning robot; a result output unit for outputting a matching result from the data recognition unit; a recognition result verifying unit for determining the accuracy of the matching result; a server communication unit for transmitting data received from the data receiving unit to a server, when the accuracy of the matching result determined by the recognition result verifying unit is lower than a predetermined level; and an action command unit for causing the self-learning robot to perform a pre-set object response action, when the accuracy of the matching result determined by the recognition result verifying unit is at least the predetermined level.

Systems and methods for data collection and detection of motor noise patterns are disclosed. A system may include a data collector communicatively coupled to at least one input channel, wherein the at least one input channel is operatively coupled to a vibration detection facility structured to detect a motor noise pattern of a motor, a library to store the detected motor noise pattern, an interface circuit structured to make the detected motor noise pattern available to a motor noise pattern data marketplace including a plurality of motor noise patterns from a plurality of motors, and a user interface for accessing at least one of the plurality of motor noise patterns of the motor noise pattern marketplace.

Systems, methods and apparatus for data monitoring are disclosed. A system may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit, a data storage circuit structured to store specifications and anticipated state information for a plurality of bearing types, a bearing analysis circuit structured to analyze the plurality of detection values relative to specifications and anticipated state information to determine a bearing performance parameter, and a response circuit structured to initiate an action in response to the bearing performance parameter.

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 human expert may initially label a white light image of teeth, and computer vision may initially label a filtered fluorescent image of the same teeth. Each label may indicate presence or absence of dental plaque at a pixel. The images may be registered. For each pixel of the registered images, a union label may be calculated, which is the union of the expert label and computer vision label. The union labels may be applied to the white light image. This process may be repeated to create a training set of union-labeled white light images. A classifier may be trained on this training set. Once trained, the classifier may classify a previously unseen white light image, by predicting union labels for that image. Alternatively, the items that are initially labeled may comprise images captured by two different imaging modalities, or may comprise different types of sensor measurements.

The present invention provides a method in which visual and non-visual semantic attributes are associated with a visual comprising preferably an input step, a preliminary visual processing step, a semantic concept processing step, a semantic context processing step, a semantic marker processing step, a semantic inheritance processing step, a semantic instance processing step, and a lexical functions step, as well as a computing device which is capable of performing said method.

A system and method for flood hazard estimation inputs a satellite elevation map and applies a machine learning model to output a geographic map representing flood hazard areas. The machine learning model is trained using a generative adversarial network (GAN) to produce an output of a deterministic hazard mapping algorithm. A GAN objective applies a loss function, reweighted to increase the importance of high hazard areas. The method retrieves a DEM topography file representing elevation data of an identified terrain, and applies a sink-filling algorithm to detect and fill sinks in the DEM topography. The algorithm subtracts the DEM elevation data to generate a filled topography, and identifies flattest regions of the filled topography. The algorithm then generates a flood hazard map by merging the filled topography and the DEM elevation data, using a weighting function that balances the detected sinks and the flattest regions of the filled topography.

In one example, a method performed by a processing system in a telecommunications network includes acquiring live footage of an event, acquiring sensor data related to the event, wherein the sensor data is collected by a sensor positioned in a location at which the event occurs, extracting an analytical statistic related to a target participating in the event, wherein the extracting is based on content analysis of the live footage and the sensor data, filtering data relating to the target based on the analytical statistic to identify content of interest in the data, wherein the data comprises the live footage, the sensor data, and data relating to historical events that are similar to the event, and generating computer-generated content to present the content of interest, wherein when the computer-generated content is synchronized with the live footage on an immersive display, an augmented reality media is produced.

Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.