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Patent Agency Ranking
公开(公告)号：WO2021045988A1
公开(公告)日：2021-03-11
申请号：PCT/US2020/048714
申请日：2020-08-31
Applicant: PROGRESS RAIL SERVICES CORPORATION
Inventor： HOWARD, Bradley , BRAND, John
Abstract: A machine learning system for maintaining distributed computer control systems for a train may include a data acquisition hub (312) communicatively connected to a plurality of sensors (304, 306, 308) 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 (316) communicatively connected to the data acquisition hub (312). The analytics server (316) may include a virtual system modeling engine (324) configured to model an actual train control system (302) comprising the distributed computer control systems, a virtual system model database (326) 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 (318) configured to monitor the real-time configuration data and the preset configuration settings. The machine learning engine (318) may warn when there is a difference between the real-time configuration data and the preset configuration settings, the difference being indicative of at least two of the distributed computer control systems being out of synchronization by more than a threshold deviation.
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公开(公告)号：WO2021045983A1
公开(公告)日：2021-03-11
申请号：PCT/US2020/048693
申请日：2020-08-31
Applicant: PROGRESS RAIL SERVICES CORPORATION
Inventor： HOWARD, Bradley , BRAND, John
Abstract: A train control system using machine learning for development of train control strategies includes a machine learning engine (318). The machine learning engine receives training data from a data acquisition hub (312), 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. Training the learning system includes providing the training data as an input to the learning function, the learning function being configured to use the at least one learning parameter to generate an output based on the input, causing the learning function to generate the output based on the input, comparing the output to the plurality of first response maneuvers to determine a difference between the output and the plurality of first response maneuvers, and modifying the at least one learning parameter to decrease the difference responsive to the difference being greater than a threshold difference.
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公开(公告)号：WO2021071778A1
公开(公告)日：2021-04-15
申请号：PCT/US2020/054243
申请日：2020-10-05
Applicant: PROGRESS RAIL SERVICES CORPORATION
Inventor： HOWARD, Bradley , BRAND, John
Abstract: A train control system (100) 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 (312), a first set of output control commands from a centralized virtual system modeling engine (324), and a second set of output control commands from a distributed virtual system modeling engine (324). 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.
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公开(公告)号：WO2021072143A1
公开(公告)日：2021-04-15
申请号：PCT/US2020/054899
申请日：2020-10-09
Applicant: PROGRESS RAIL SERVICES CORPORATION
Inventor： HOWARD, Bradley , BRAND, John
Abstract: A train control system (100) 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 (312), 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 (324). The machine learning engine trains a learning system using the training data to enable the machine learning engine to predict when a locomotive (108, 110) of the train (102) will enter a geo-fence where communication between the edge-based computer processing system and the centralized computer processing system will be inhibited.
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公开(公告)号：WO2021071776A1
公开(公告)日：2021-04-15
申请号：PCT/US2020/054239
申请日：2020-10-05
Applicant: PROGRESS RAIL SERVICES CORPORATION
Inventor： HOWARD, Bradley , BRAND, John
Abstract: A train control system (100) controls the ramp rate at which a train (102) accelerates after braking. A machine learning engine receives training data from a data acquisition hub (312), including a plurality of input conditions of the train (102) and a plurality of outputs associated with the input conditions. A virtual system modeling engine (324) simulates in-train forces and train (102) 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.
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