Abstract: A method for facilitating dynamic-transparency chart overlays via a graphical user interface is provided. The method includes displaying, via a client device, the graphical user interface, the graphical user interface including a graphical element to receive an input; receiving, via the graphical user interface, a request relating to an overlay request that corresponds to a visualization of a chart; retrieving data corresponding to the chart; converting the data into a structured data set based on a parameter; generating an overlay chart based on the structured data set, the overlay chart corresponding to a superimposition of the chart; and displaying, via the client device, the overlay chart on the graphical user interface in response to the request.

Abstract: A machine learning system for maintaining distributed computer control systems for a train may include a data acquisition hub communicatively connected to a plurality of sensors configured to acquire real-time configuration data from one or more of the computer control systems. The machine learning system may also include an analytics server communicatively connected to the data acquisition hub. The analytics server may include a virtual system modeling engine configured to model an actual train control system comprising the distributed computer control systems, a virtual system model database configured to store one or more virtual system models of the distributed computer control systems, wherein each of the one or more virtual system models includes preset configuration settings for the distributed computer control systems, and a machine learning engine configured to monitor the real-time configuration data and the preset configuration settings.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: Systems and methods for adjusting operation of a train are disclosed. A method may include: receiving one or more infrared images of a brake line of the train; detecting one or more leaks of the brake line based on the one or more infrared images; and adjusting a brake command based on the detected one or more leaks.

Abstract: A method for facilitating dynamic-transparency chart overlays via a graphical user interface is provided. The method includes displaying, via a client device, the graphical user interface, the graphical user interface including a graphical element to receive an input; receiving, via the graphical user interface, a request relating to an overlay request that corresponds to a visualization of a chart; retrieving data corresponding to the chart; converting the data into a structured data set based on a parameter; generating an overlay chart based on the structured data set, the overlay chart corresponding to a superimposition of the chart; and displaying, via the client device, the overlay chart on the graphical user interface in response to the request.

Abstract: A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: A system may include a data acquisition hub connected to databases and sensors associated with locomotives, systems, or components of a train and configured to acquire real-time and historical configuration, structural, and operational data in association with inputs derived from real time and historical contextual data relating to a plurality of trains. The system may include a virtual system modeling engine configured to receive results of a non-destructive evaluation of a train component, simulate in-train forces, determine a predicted time of failure for the train component based on an evaluation of stresses that have already been applied to the component and expected future stresses, and implement repair, replacement, or operational protocols for the train component before or at a repair facility that will be reached by the train ahead of a predetermined minimum threshold time period before the predicted time of failure.

Abstract: A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: A train control system uses machine learning for implementing handovers between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, receives a centralized train control model from a centralized virtual system modeling engine, and receives an edge-based train control model from an edge-based virtual system modeling engine. The machine learning engine trains a learning system using the training data to enable the machine learning engine to predict when a locomotive of the train will enter a geo-fence where communication between the edge-based computer processing system and the centralized computer processing system will be inhibited.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: Systems and methods for adjusting operation of a train are disclosed. A method may include: receiving one or more infrared images of a brake line of the train; detecting one or more leaks of the brake line based on the one or more infrared images; and adjusting a brake command based on the detected one or more leaks.

Abstract: A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.

Abstract: A train control system uses machine learning for implementing handovers between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, receives a centralized train control model from a centralized virtual system modeling engine, and receives an edge-based train control model from an edge-based virtual system modeling engine. The machine learning engine trains a learning system using the training data to enable the machine learning engine to predict when a locomotive of the train will enter a geo-fence where communication between the edge-based computer processing system and the centralized computer processing system will be inhibited.

Abstract: A system may include a data acquisition hub connected to databases and sensors associated with locomotives, systems, or components of a train and configured to acquire real-time and historical configuration, structural, and operational data in association with inputs derived from real time and historical contextual data relating to a plurality of trains. The system may include a virtual system modeling engine configured to receive results of a non-destructive evaluation of a train component, simulate in-train forces, determine a predicted time of failure for the train component based on an evaluation of stresses that have already been applied to the component and expected future stresses, and implement repair, replacement, or operational protocols for the train component before or at a repair facility that will be reached by the train ahead of a predetermined minimum threshold time period before the predicted time of failure.

Abstract: A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

Abstract: A train control system controls the ramp rate at which a train accelerates after braking. A machine learning engine receives training data from a data acquisition hub, including a plurality of input conditions of the train and a plurality of outputs associated with the input conditions. A virtual system modeling engine simulates in-train forces and train operational characteristics using physics-based equations, kinematic or dynamic modeling of behavior of the train or components of the train during operation of the train when the train is accelerating after braking, and inputs derived from stored historical contextual data related to the train. The machine learning engine trains a learning system using the training data to generate an output based on an input using a learning function including at least one learning parameter. The learning parameter is modified as needed to improve the accuracy of the learning function in generating the output.

Abstract: A machine learning system for maintaining distributed computer control systems for a train may include a data acquisition hub communicatively connected to a plurality of sensors configured to acquire real-time configuration data from one or more of the computer control systems. The machine learning system may also include an analytics server communicatively connected to the data acquisition hub. The analytics server may include a virtual system modeling engine configured to model an actual train control system comprising the distributed computer control systems, a virtual system model database configured to store one or more virtual system models of the distributed computer control systems, wherein each of the one or more virtual system models includes preset configuration settings for the distributed computer control systems, and a machine learning engine configured to monitor the real-time configuration data and the preset configuration settings.

Abstract: A train control system using machine learning for development of train control strategies includes a machine learning engine. The machine learning engine receives training data from a data acquisition hub, including a plurality of first input conditions and a plurality of first response maneuvers associated with the first input conditions. The machine learning engine trains a learning system using the training data to generate a second response maneuver based on a second input condition using a learning function including at least one learning parameter.