An anomalous region detection system includes a controller configured to, receive data being grouped in patches, encode, via parameters of an encoder, the data to obtain a series of local latent representations for each patch, calculate, for each patch, a Contrastive Predictive Coding (CPC) loss from the local latent representations to obtain updated parameters, update the parameters of the encoder with the updated parameters, score each of the series of the local latent representations, via the Contrastive Predictive Coding (CPC) loss, to obtain a score associated with each patch, smooth the score to obtain a loss region, mask the data associated with the loss region to obtain verified data, and output the verified data.

A data processing system includes a host and an accelerator. The host transmits, to the accelerator, input data together with data identification information based on a data classification criterion. The accelerator classifies the input data as any one of feature data, a parameter, and a bias based on the data identification information when the input data is received from the host, distributes the input data, performs pre-processing on the feature data, and outputs computed result data to the host or feeds the result data back so that computation processing is performed on the result data again.

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.

A system for dynamic conditional guidance using artificial intelligence. The system includes a computing device, designed and configured to c calculate a diagnostic output using a biological extraction related to a user, and a first machine-learning process, wherein the diagnostic output identifies a prognostic label and an ameliorative label; classify, using a physiological classifier and a first classification algorithm, the diagnostic output to a physiological state for the user; generate a vector output for the physiological state for the user, using a clustering algorithm; receive a user input generated in response to the diagnostic output; update the vector output using the user input; and identify a recommendation for the user, utilizing the updated vector output.

A neural network architecture and a method for determining a stress of a structure. The neural network architecture includes a first neural network and a second neural network. A neuron of last hidden layer of the first neural network is connected to a neuron of a last hidden layer of the second neural network. A first data set is input into the first neural network. A second data set is input into the second neural network. Data from the last hidden layer of the first neural network is combined with data from the last hidden layer of the second neural network. The stress of the structure is determined from the combined data.

A data processing system includes a host and an accelerator. The host transmits, to the accelerator, input data together with data identification information based on a data classification criterion. The accelerator classifies the input data as any one of feature data, a parameter, and a bias based on the data identification information when the input data is received from the host, distributes the input data, performs pre-processing on the feature data, and outputs computed result data to the host or feeds the result data back so that computation processing is performed on the result data again.

A novel system includes an intelligent process anomaly detection and trend projection system which is configured to train artificial intelligence and machine learning systems for anomaly prediction in industrial systems according to some embodiments. In some embodiments, such intelligent process anomaly detection and trend projection system is configured to determine an estimated remaining useful life of an industrial asset. For example, in some embodiments, the system is configured to identify a degradation part of the signal and a normal part of the signal; separate the degradation part of the signal from the normal part of the signal; identify one or more patterns of a degradation part of the signal and the normal part of the signal; and determine an anomaly prediction based on the one or more patterns.

Disclosed is an intelligent control system of spunlace production line, which includes a data acquiring module, which is used for acquiring and storing real-time production line data; the production line data includes cotton feeding roller value, real-time moisture value, real-time speed value and real-time gram weight value; the data process module is used for classify and controlling that production line data, and giving the adjustment opinions of the cotton feeding roller parameters; the parameter control module is used for verifying the parameter adjustment opinions and applying the opinions to the control system; the data acquiring module, the data processing module and the parameter control module are connected in sequence.

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.

A method for the prevention of failures in the operation of a motorized door. At least one sensor provides time series sensor data of at least one variable of a motorized door. The time series sensor data is used for machine learning in order to monitor, detect and/or predict anomalies in the operation of the motorized door. There is also described a monitoring system for a motorized door that is configured to carry out the method.