Fastening assembly for fastening a test device holder to a force-measuring apparatus, force-measuring apparatus having a fastening assembly of this type, test device holder, and slide part for a force-measuring apparatus

    公开(公告)号:US12061175B2

    公开(公告)日:2024-08-13

    申请号:US17312284

    申请日:2019-12-06

    发明人: Michael Wolff

    摘要: The invention relates to a fastening assembly (3) for fastening a test device holder (5) to a force measuring apparatus (1), having a test device holder (5) and a slide part (7) which can be or is arranged on a force measurement tower (9) of the force measuring apparatus (1) in such a way that the slide part can move in the vertical direction of the force measurement tower (9). The test device holder (5) has at least one position defining element (11), and the slide part (7) has at least one counter position defining element (13). The position defining element (11) and the counter position defining element (13) are designed to fix the position of the test device holder (5) relative to the slide part (7) in at least one direction, selected from the vertical direction of the force measurement tower (9) and a direction perpendicular to the vertical direction, and at the same time to allow rotation of the test device holder (5) relative to the slide part (7) about an axis of rotation (D) defined by the position defining element (11) and/or the counter position defining element (13). The test device holder (5) has a first angle adjustment device (15) and the slide part (7) has a second angle adjustment device (17), which are designed to adjust and preferably to fix the angle of the test device holder (5) relative to the slide part (7) about the axis of rotation (D). The test device holder (5) has at least one fixing element (19) and the slide part (7) has at least one counter fixing element (21). The fixing element (19) and the counter fixing element (21) are designed to fix the test device holder (5) on the slide part (7).

    Vehicle repositioning on mobility-on-demand platforms

    公开(公告)号:US12061090B2

    公开(公告)日:2024-08-13

    申请号:US17058571

    申请日:2020-07-10

    IPC分类号: G01C21/34 G06N3/08

    CPC分类号: G01C21/3461 G06N3/08

    摘要: Deep reinforcement learning may be used for vehicle repositioning on mobility-on-demand platforms. Information may be obtained. The information may include a current location of a vehicle on a ride-sharing platform. A set of paths originated from the current location of the vehicle may be obtained. Each of the set of paths may have a length less than a preset maximum path length. A set of expected cumulative rewards along the set of paths may be obtained based on a trained deep value-network. A best path from the set of paths may be selected based on a heuristic tree search of the set of expected cumulative rewards. A next step along the best path may be recommended as a reposition action for the vehicle.

    Estimating target heading using a single snapshot

    公开(公告)号:US12055623B2

    公开(公告)日:2024-08-06

    申请号:US18363059

    申请日:2023-08-01

    发明人: Kan Fu Ji Jia Yu Liu

    摘要: Provided herein is a system and method to determine a heading of a target. The system includes a radar sensor that obtains a snapshot of radar data comprising Doppler velocities and spatial positions of a plurality of detection points of a target, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform conducting a first estimation of a heading of the target based on the spatial positions; conducting a second estimation of the heading of the target based on the Doppler velocities; and obtaining a combined estimation of the heading of the target based on a weighted sum of the first estimation and the second estimation.

    Unsupervised segmentation of a univariate time series dataset using motifs and shapelets

    公开(公告)号:US12050626B2

    公开(公告)日:2024-07-30

    申请号:US17991500

    申请日:2022-11-21

    IPC分类号: G06F16/28 G06N20/00

    CPC分类号: G06F16/285 G06N20/00

    摘要: Systems and methods are provided for receiving a time series dataset from a monitored processor and group the dataset into a plurality of clusters. Using an unsupervised machine learning model, the system may combine a subset of the plurality of clusters by data signature similarities to form a plurality of motifs and combine the plurality of motifs into one or more shapelets. In some examples, the system may train a supervised machine learning model using the plurality of motifs and the one or more shapelets as input to the supervised machine learning model. The system can perform various actions in response to labelling the time series dataset, including predicting a second time series dataset, determining that a monitored processor corresponds with an overutilization at a particular time, or suggesting a reduction of additional utilization of the monitored processor.