Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, receiving a plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, clustering a matrix of time series data, generated from the input time series of data, into a predetermined number of hyperplanes, extracting extracted features that are indicative of an operation of a vehicle system from a sparse temporal matrix based on data point behavior with respect to two or more hyperplanes within the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, the sparse temporal matrix being based on the predetermined number of hyperplanes; and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: Described is a system for identifying communication behavior patterns in communication activity time series. For each pair of variables in the communication activity time series, the system determines a transfer entropy measure, an effective transfer entropy measure from a randomly reordered version of the communication activity time series, and a partial effective transfer entropy measure. A dependency matrix is generated using pair-wised effective transfer entropy measures and partial effective transfer entropy measures, where each element in the matrix represents a total influence of a communication activity time series on another communication activity time series in the future. The dependency matrix is compared with dependency matrices generated from a predefined set of communication patterns to identify the communication behavior pattern.

Abstract: Systems and methods for fault prediction through a Bayesian framework are provided. Fault prediction for a valve system may be provided by generating a Bayesian framework by collecting a plurality of historical parameters related to opening and closing of a valve across a plurality of operational legs; generating a plurality of historical feature metrics based on the plurality of historical parameters; in response to detecting a fault, defining a prefault state corresponding to the historical feature metrics; monitoring a plurality of operational parameters related to opening and closing of the valve during a given operational phase of an operational leg; generating a plurality of operational feature metrics based on the plurality of operational parameters monitored during the given operational phase; and in response to determining, using the generated Bayesian framework, that the operational feature metrics indicate the prefault state of the subsystem, generating a notification.

Abstract: Described is a system for discriminating between individual accounts and organization accounts in a social media network. The system processes data from a social media network comprising a set of user accounts. A directed mention network, a bi-directed mention network, and a set of computer-observable network metrics are generated. A classification model is trained using the set of computer-observable network metrics and a set of ground-truth user accounts. The trained classification model is used to classify each user account in the set of user accounts as an individual account or an organization account, resulting in a set of classified user accounts.

Abstract: An apparatus for detecting fault states of an aircraft is provided. The apparatus receives training data including operational parameters of the aircraft operating over a plurality of training flight legs and applies a first clustering algorithm separately to the training data to produce first clustered data. The apparatus applies a second clustering algorithm to the first clustered data to produce second clustered data that indicates a plurality of states describing behavior of the aircraft operating over the training legs and applies a third clustering algorithm to identify one or more fault states of the aircraft from the plurality of states based on the training data and the second clustered data. The apparatus receives messages carrying data from the aircraft over a digital datalink, detects a fault state of the one or more fault states from the data, and generates an alert of the fault state.

Abstract: An aircraft component failure prediction apparatus including a database and an aircraft maintenance controller coupled to the database. The controller classifies operational data, in a first classification, as corresponding with at least one maintenance message to form at least one classified multidimensional operation data matrix including classified data. The controller classifies the classified data of the at least one classified multidimensional data matrix, in a second classification, to predict an occurrence of a future maintenance message for the aircraft component. The controller preprocesses the at least one classified multidimensional operation data matrix, and generates an output of at least one partial dependency function that explains prediction of the occurrence of the future maintenance message.

Abstract: Described is a system for predicting system trajectories toward critical transitions. The system transforms a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series. Then pair-wise time series of a transfer entropy (TE) measure are determined, wherein the TE measure quantifies the amount of information transfer from a source to a destination in the complex system. An associative transfer entropy (ATE) measure is determined which decomposes the pair-wise time series of TE to associative states of asymmetric, directional information flows, wherein the ATE measure is comprised of an ATE+ influence class and a ATE? influence class. The system estimates ATE+, TE, and ATE? trajectories over time, and at least one of the ATE+, TE, and ATE? trajectories is used to predict a critical transition in the complex system.

Abstract: Described is a system for event prediction on microblogs. In operation, the system receives a social media post stream from a social network to generate and maintain a social network database and an audit trail database. New social media posts are filtered to determine if the new social media posts are related to a possible future event. The new social media posts are compared against the social network database and audit trail database to provide a quality score, the quality score representing a likelihood that at least one of the new social media posts is a true prediction of a future event. If the quality score is determined to be true, the detection is published as a true prediction of a future event.

Abstract: Described is system for inferring networks service dependencies. The system detects a set of network services from a set of network data. The system additionally detects a set of network service profiles in the set of network data. A network switching topology of the network is then determined. Finally, network service dependencies are inferred by analyzing a dependency structure of the network using at least one of the set of network services, the set of network service profiles, and the network topology.

Abstract: Described is a system for inducing a desired behavioral effect using an electrical current stimulation. A brain monitoring subsystem includes monitoring electrodes for sensing brain activity, and a brain stimulation subsystem includes stimulating electrodes for applying an electrical current stimulation. Multi-scale distributed data is registered into a graphical representation. The system identifies a sub-graph in the graphical representation and maps the sub-graph onto concept features, generating a concept lattice which relates the concept features to a behavioral effect. Finally, an electrical current stimulation to be applied to produce the behavioral effect is determined.

Abstract: Described is a system for extracting multi-scale hierarchical clustering on customer observables (COs) data in a vehicle. The system selects a parameter for a set of incident data of COs data. Simplicial complexes are generated from the COs data based on the selected parameter. Face networks are generated from the simplicial complexes. For each face network, a set of connected components is extracted. Each connected component is transformed to a cluster of related COs, resulting in a first extracted relation between COs. The first extracted relation is used to automatically generate an alert at a client device when a second extracted relation different from the first extracted relation results from the transformation.

Abstract: A method for estimating the impact of an event includes: receiving social media posts, each of the social media posts including content, a timestamp, and a geolocation; grouping the social media posts by geographic region in accordance with the geolocation associated with the social media post and by time window in accordance with the timestamp associated with the social media post; extracting feature vectors from the social media posts, each of the feature vectors corresponding to one group of social media posts; supplying the feature vectors to one or more models of events to generate one or more classifications of the groups of social media posts, each of the models of events corresponding to a different kind of event, and the classifications of the groups indicating the level of impact of the different kinds of events; and operating a device based on the classifications of the groups of social media posts.

Abstract: A system for monitoring aircraft operational messages is provided. The system includes a computing device including a processor in communication with a memory. The computing device is programmed to receive a plurality of historical messages for a plurality of aircraft. Each message of the plurality of historical messages is a message from one of the plurality of aircraft. The computing device is also programmed to receive a plurality of historical maintenance operations performed on the plurality of aircraft, compare the plurality of historical messages to the plurality of historical maintenance operations to determine at least one message type associated with at least one maintenance operation type, and generate a plurality of message type correlations between message types and maintenance operation types based on the comparison.

Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, obtaining a plurality of sensor signals corresponding to the predetermined characteristic, receiving the plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, generating, a matrix of time series data based on the input time series of data, clustering the matrix of time series data based on predetermined clustering criteria into a predetermined number of clusters, generating a sparse temporal matrix based on the predetermined number of clusters, extracting extracted features that are indicative of an operation of a vehicle system from the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: Described is system for accurate user alignment across multiple online social media platforms. Out of textual messages from multiple user accounts of a first social media platform, the system identifies a set of textual messages from a first user account and a second user account of the first social media platform, each textual message in the set of textual messages comprising a set of specific character strings. The set of specific character strings represents a link to a post on a second social media platform, resulting in linked messages, the post originating from a linked account of the second social media platform. Either the first user account or the second user account is selected as an associated account by determining which originated the greater number of messages in the set of textual messages. A map component associated with a user identity that includes the associated account and the linked account is generated.

Abstract: Described is a system for early detection of events via social media mining. The system receives, as input, social media blog posts comprising textual data. The system processes the social media blog posts through a cascade of filters. The cascade of filters comprises an event term detection filter, a location term detection filter following the event term detection filter, and a future date detection filter following the location term detection filter. A plurality of candidate social media blog posts describing an event of interest on a future date is output to a user for further analysis.

Abstract: A method and apparatus for maintaining a vehicle, such as an aircraft. A plurality of maintenance messages generated during operation of the vehicle are stored to form a plurality of stored maintenance messages. The stored maintenance messages are filtered to remove from the stored maintenance messages those maintenance messages that are correlated to minimum equipment list actions to form filtered stored maintenance messages. A predicted maintenance message is generated from the filtered stored maintenance messages by applying a machine learning algorithm to the filtered stored maintenance messages. The predicted maintenance message may be used to perform a maintenance operation on the vehicle.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Abstract: An aircraft fault detection system including at least one aircraft data logging device configured to capture parametric flight data from at least one aircraft subsystem, and an aircraft controller coupled to the data logging device. The controller being configured to group the parametric flight data from the at least one aircraft subsystem into a plurality of test states, one or more of the test states being different from other test states in the plurality of states, generate at least one test transition matrix based on the plurality of test states and determine anomalous behavior of the at least one aircraft subsystem based on the at least one test transition matrix, and forecast faults within the at least one aircraft subsystem based on the anomalous behavior of the at least one aircraft subsystem determined from the at least one test transition matrix.

Abstract: An aircraft component failure prediction apparatus including a database and an aircraft maintenance controller coupled to the database. The controller classifies operational data, in a first classification, as corresponding with at least one maintenance message to form at least one classified multidimensional operation data matrix including classified data. The controller classifies the classified data of the at least one classified multidimensional data matrix, in a second classification, to predict an occurrence of a future maintenance message for the aircraft component. The controller preprocesses the at least one classified multidimensional operation data matrix, and generates an output of at least one partial dependency function that explains prediction of the occurrence of the future maintenance message.