Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

The Boeing Company

Aruna Jammalamadaka

Tsai-Ching Lu

Rajan Bhattacharyya

Nigel D Stepp

Dmitriy V Korchev

Samuel D Johnson

David J Huber

Jiejun Xu

Kenji Yamada

Alice A Murphy

Chong Ding

Victor Ardulov

Derek Samuel Fok

Douglas Peter Knapp

Alexander Norman Waagen

Michael J Daily

Charles E Martin

Kang-Yu Ni

Rashmi N Sundareswara

Kirby Joe Keller

Dana M Warmsley

Sasha Strelnikoff

Hao-Yuan Chang

Krishna Bathina

Hyun J Kim

A method, apparatus, system, and computer program product for managing a platform. Sensor information for a platform health of the platform is received from a sensor system for the platform. The sensor information for the platform health of the platform is sent by a computer system into a machine learning model trained using historical sensor information indicating a historical platform health and historical context information corresponding to the historical sensor information in which the historical context information is for a set of operating conditions. A remaining useful life of a component in the platform is received by the computer system from the machine learning model.

Aircraft maintenance of line replaceable units

Described is a system for generating and displaying decision recommendations. The system receives historical observations and courses of action corresponding to the historical observations. A conditional density between the historical observations and the courses of action is estimated, and the historical observations are clustered according to the conditional density to obtain observational clusters. Observational clusters are merged to determine a causal partition having corresponding causal features which map to various courses of action. A new observation is mapped to the causal partition and a ranking of courses of action for the new observation is computed. A visualization of causal features and a ranking of courses of actions corresponding to the new observation is provided to a display.

Decision recommendation via causal feature display

A method, apparatus, system, and computer program product for managing a platform. A computer system generates a training dataset comprising historical metric values from historical sensor information for a set of metrics for a part and historical maintenance events for the part. The computer system trains a machine learning model using the training dataset. The computer system determines different maintenance thresholds for maintenance parameters for a metric in the set of metrics for performing maintenance on the part using the machine learning model trained with the training dataset. The computer system selects maintenance thresholds for the maintenance parameters from the maintenance thresholds meeting an objective to form a maintenance plan. The maintenance plan is used to determine when a maintenance action is needed for the part.

Vehicle health management using a counterfactual machine learning model to optimize maintenance parameters

Described is a system for producing a causal map from a body of text. The system receives multiple textual documents as input. Pairs of cause-effect phrases are extracted from the textual documents and embedded into a vector space. The embedded data is clustered into clusters using a probabilistic technique. A causal map having nodes and edges is generated from the clusters. Using the causal map, causal connections between clusters are obtained, where each node represents an event and each edge represents a causal relationship between events. The causal map is provided as an interactive graph.

Method and system for event prediction via causal map generation and visualization

Described is a system for analyzing time series data. A sequence of symbols is generated from a set of time series input data related to a moving vehicle using automatic segmentation. A grammar is extracted from the sequence of symbols, and the grammar is a subset of a probabilistic context-free grammar (PCFG). Using the grammar, time series input data can be analyzed, and a prediction of the vehicle's movement can be made. Vehicle operations for an autonomous vehicle are determined using the prediction.

Method for time series structure discovery

Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

System and method for learning contextually aware predictive key phrases

A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

System and method for human-machine hybrid prediction of events

A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.

Systems and methods for forecast alerts with programmable human-machine hybrid ensemble learning

Described is a system for specifying control of a device based on a Bayesian network model. The system includes a Bayesian neuromorphic compiler having a network composition module having probabilistic computation units (PCUs) arranged in a hierarchical composition containing multi-level dependencies. The Bayesian neuromorphic compiler receives a Bayesian network model as input and produces a spiking neural network topology and configuration that implements the Bayesian network model. The network composition module learns conditional probabilities of the Bayesian network model. The system computes a conditional probability and controls a device based on the computed conditional probability.

Network composition module for a bayesian neuromorphic compiler

Described is a system for large-scale event prediction and a corresponding response. The system, using an agent-based model, predicts how many users (agent accounts) on a social media platform will become activists related to a large-scale event. This process is accomplished using both Before and During models. Before the large-scale event, the system operates to generate agent attributes and a posting network based on posts on the social media platform. During the large-scale event and based on the agent attributes and posting network, the system determines if a social media user (agent account) will become an activist of the large-scale event and a corresponding magnitude of the large-scale event. Depending on the magnitude, the system can implement a responsive measure and control a device based on the prediction of the activists.

System and method for event prediction using online social media

Described is a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.

Spiking neural network for probabilistic computation

Described is a system for performing probabilistic computations on mobile platform sensor data. The system translates a Bayesian model representing input mobile platform sensor data to a spiking neuronal network unit that implements the Bayesian model. Using the spiking neuronal network unit, conditional probabilities are computed for the input mobile platform sensor data, where the input mobile platform sensor data is a time series of mobile platform error codes encoded as neuronal spikes. The neuronal spikes are decoded and represent a mobile platform failure mode. The system causes the mobile platform to initiate a mitigation action based on the mobile platform failure mode.

Programming model for a bayesian neuromorphic compiler

The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method is operative to receive an input indicative of a training event, segmenting the driving episode into a plurality of time steps, generate a parse tree in response to each time step, and generate a most probable parse tree from a combination of the generated parse trees.

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Agoura Hills, CA, United States of America

Aruna Jammalamadaka