Abstract: A method and system are provided for training a machine learning (ML) system for predicting delays in processing pipelines. In one embodiment, the method includes receiving labelled historical data pertaining to a pipeline, the labelled data including trigger objects initiating the pipeline and one or more processing times corresponding to one or more stages of the pipeline. The method includes identifying features associated with the trigger objects, formatting the labelled data and, randomly splitting the formatted labelled data into a full training dataset and a testing dataset. Additionally, the method includes distributing the full training dataset into several partial datasets and, in an ensemble ML system, training each of several ML subsystems using a respective partial dataset to provide a respective individual inference model predicting respective processing times, and deriving and storing an ML model for prediction of delays by aggregating the individual inference models.

Abstract: A method (500) for toggling multi-network connectivity of a mobile device (110) includes, for the mobile device simultaneously connected to one or more carrier-mediated wireless networks (120) associated with a network operator (70), executing a graphical user interface that renders a status graphic (320) indicating the mobile device is currently connected to at least one carrier-mediated wireless network associated with the network operator, and an interactive graphic (330) for selecting between disabling and enabling connections (122) between the mobile device and carrier-mediated wireless networks associated with the network operator.

Abstract: A method (500) for demarcating carrier-mediated and user-mediated networks includes, for a mobile device (110) simultaneously connected to a first wireless network (120) and a second wireless network (120), determining whether the first wireless network is associated with a same network operator (70) as the second wireless network. When the operator is the same, the method includes establishing a virtual private network (VPN) (30) between the mobile device and a remote server (130). The VPN includes a first VPN connection (122) that connects the mobile device to the remote server over the first wireless network and a second VPN connection (122) that connects the mobile device to the remote server over the second wireless network. The method includes transmitting data (50) from the mobile device to the remote server over the VPN via one of the VPN connections. The remote server routes the data to a destination server (60).

Abstract: A multi-degree-of-freedom electromagnetic machine includes a spherical stator, an armature, a payload mount assembly, and a payload. The spherical stator has a first axis of symmetry, a second axis of symmetry, and a third axis of symmetry that are disposed perpendicular to each other. The armature is spaced apart from, and surrounds at least a portion of, the spherical stator, and is mounted for rotation about the first and second axes of symmetry. The payload is spaced apart from the spherical stator and the armature and is coupled to the payload mount assembly and is mounted to rotate about a payload rotational axis that is parallel to the first axis of symmetry and perpendicular to the second axis of symmetry. The payload is disposed such that its center of gravity is at a position where the second axis of symmetry and the payload rotational axis intersect.

Abstract: A method and system are provided for training a machine learning (ML) system for predicting delays in process pipelines. According to one embodiment, a method for training includes receiving labelled historical data pertaining to a pipeline, where the labelled data includes trigger objects initiating the pipeline and one or more processing times corresponding to one or more stages of the pipeline. The method also includes identifying one or more features associated with the trigger objects, formatting the labelled data and, using a pseudorandom generator, randomly splitting the formatted labelled data into a full training dataset and a testing dataset.

Abstract: A solenoid actuator includes a housing assembly, a bobbin assembly, a coil, an armature, and an anti-rotation structure. The bobbin assembly is disposed at least partially within the housing assembly and includes a return pole and a yoke. The yoke has an inner surface that defines an armature cavity. The coil is disposed within the housing assembly and is wound around at least a portion of the bobbin assembly. The armature is disposed within the armature cavity and is axially movable relative to the yoke. The anti-rotation structure is disposed within the housing assembly and engages at least a portion of the armature. The armature and the anti-rotation structure each have at least one feature formed thereon that mate with each other and thereby prevent rotation of the armature.

Abstract: A solenoid actuator includes a housing assembly, a bobbin assembly, a coil, an armature, and an anti-rotation structure. The bobbin assembly is disposed at least partially within the housing assembly and includes a return pole and a yoke. The yoke has an inner surface that defines an armature cavity. The coil is disposed within the housing assembly and is wound around at least a portion of the bobbin assembly. The armature is disposed within the armature cavity and is axially movable relative to the yoke. The anti-rotation structure is disposed within the housing assembly and engages at least a portion of the armature. The armature and the anti-rotation structure each have at least one feature formed thereon that mate with each other and thereby prevent rotation of the armature.

Abstract: A high vibration, high-cycle, pulse width modulated (PWM) solenoid actuated valve assembly includes a valve housing and a solenoid actuator. The solenoid actuator is coupled to the valve housing and includes a solenoid housing, a bobbin assembly, a coil, an armature, a glide structure, an actuation rod, and an anti-rotation guide structure. The glide structure is disposed at least partially within the armature and between the armature and the inner surface of the yoke. The actuation rod is coupled to, and is axially movable with, the armature, it also extends into the valve housing and has a ball valve integrally formed thereon. The anti-rotation guide structure is disposed within the valve housing and surrounds at least a portion of the actuation rod. At least the actuation rod and the anti-rotation guide each have structural features formed thereon that mate with each other and prevent rotation of the actuation rod and the armature.

Abstract: A high vibration, high-cycle, pulse width modulated (PWM) solenoid actuated valve assembly includes a valve housing and a solenoid actuator. The solenoid actuator is coupled to the valve housing and includes a solenoid housing, a bobbin assembly, a coil, an armature, a glide structure, an actuation rod, and an anti-rotation guide structure. The glide structure is disposed at least partially within the armature and between the armature and the inner surface of the yoke. The actuation rod is coupled to, and is axially movable with, the armature, it also extends into the valve housing and has a ball valve integrally formed thereon. The anti-rotation guide structure is disposed within the valve housing and surrounds at least a portion of the actuation rod. At least the actuation rod and the anti-rotation guide each have structural features formed thereon that mate with each other and prevent rotation of the actuation rod and the armature.