A system for monitoring an oil and gas process includes a data acquisition circuit structured to interpret a plurality of detection values corresponding to input received from a detection package which includes at least one of a plurality of input sensors each operatively coupled to at least one of a plurality of components of an industrial production process; a data analysis circuit structured to analyze a subset of the plurality of detection values to determine a status parameter; and an analysis response circuit structured to adjust the detection package in response to the status parameter, wherein the plurality of available sensors have at least one distinct sensing parameter selected from the sensing parameters consisting of: input ranges, sensitivity values, locations, reliability values, duty cycle values, sensor types, and maintenance requirements.

The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

Learning means 701 learns a model for identifying an object indicated by data by using training data. First identification means 702 identifies the object indicated by the data by using the model learned by the learning means 701. Second identification means 703 identifies the object indicated by the data as an identification target used by the first identification means 702 by using a model different from the model learned by the learning means 701. The learning means 701 re-learns the model by using the training data including the label for the data determined based on the identification result derived by the second identification means 703 and the data.

A plurality of recognition units respectively recognize image data using a learned model and output degrees of reliability corresponding to classes regarded as recognition targets by respective recognition units. A reliability generation unit generates degrees of reliability corresponding to a plurality of target classes based on the degrees of reliability output from the plurality of recognition units. A target model recognition unit recognizes the same image data as that recognized by the recognition units, by using a target model, and adjusts parameters of the target model in order to match the degrees of reliability corresponding to the target classes generated by a generation unit that outputs degrees of reliability corresponding to the target classes with the degrees of reliability corresponding to the target classes output from the target model recognition unit.

A method is provided for classifying objects. The method detects objects in one or more images. The method tags each object with multiple features. Each feature describes a specific object attribute and has a range of values to assist with a determination of an overall quality of the one or more images. The method specifies a set of training examples by classifying the overall quality of at least some of the objects as being of an acceptable quality or an unacceptable quality, based on a user's domain knowledge about an application program that takes the objects as inputs. The method constructs a plurality of first-level classifiers using the set of training examples. The method constructs a second-level classifier from outputs of the first-level automatic classifiers. The second-level classifier is for providing a classification for at least some of the objects of either the acceptable quality or the unacceptable quality.

The technology disclosed processes a first input through a first neural network and produces a first output. The first input comprises first image data derived from images of analytes and their surrounding background captured by a sequencing system for a sequencing run. The technology disclosed processes the first output through a post-processor and produces metadata about the analytes and their surrounding background. The technology disclosed processes a second input through a second neural network and produces a second output. The second input comprises third image data derived by modifying second image data based on the metadata. The second image data is derived from the images of the analytes and their surrounding background. The second output identifies base calls for one or more of the analytes at one or more sequencing cycles of the sequencing run.

Described herein are systems and methods of determining primary sites from biomedical images. A computing system may identify a first biomedical image of a first sample from one of a primary site or a secondary site associated with a condition in a first subject. The computing system may apply the first biomedical image to a site prediction model comprising a plurality of weights to determine the primary site for the condition. The computing system may store an association between the first biomedical image and the primary site determined using the site prediction model.

Systems, methods and apparatus for modifying a data collection trajectory for conveyors are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of conveyor types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a conveyor performance parameter. A response circuit may initiate an action in response to the conveyor performance parameter.

Systems, methods and apparatus for modifying a data collection trajectory for centrifuges are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of centrifuge types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a centrifuge performance parameter. A response circuit may initiate an action in response to the centrifuge performance parameter.

Systems, devices and methods for bearing analysis in an industrial environment are disclosed. A data acquisition circuit structured to interpret a plurality of detection values corresponding to a plurality of input sensors coupled to the data acquisition circuit, a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time, and a bearing analysis circuit to analyze buffered detection values relative to specifications and anticipated state information resulting in at least one bearing parameter are described. Analysis may include filtering the detection values through a high pass filter, identifying rapid changes in detection values, identifying frequencies at which spikes occur and comparing frequencies and spikes in amplitude relative to an anticipated state information and specification.