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公开(公告)号:EP4462403A1
公开(公告)日:2024-11-13
申请号:EP23173047.4
申请日:2023-05-12
发明人: HELLGREN, Jonas , KOJCHEV, Stefan
IPC分类号: G08G1/127 , B60W60/00 , G06N3/00 , G06N3/006 , G06N3/02 , G06N3/08 , G06N5/01 , G06N7/01 , G06N20/00 , G06Q10/047 , G06Q10/08 , G08G1/00
摘要: A computer-implemented traffic planning method (100) for controlling a plurality of vehicles which are movable among multiple shared resources, comprising:
receiving (no) a transport mission;
generating (112) a root node representing an initial resource occupancy of the vehicles;
sequentially generating (114) a search tree from the root node, in which each edge represents a motion command and each node represents a resource occupancy, wherein each node is associated with a score including an anticipated reward for fulfilling the transport mission;
identifying (116) a target node with an acceptable score; and
deriving (118) a planned sequence of motion commands corresponding to a path to the target node, wherein:
child nodes of a leaf node are generated (114.4) subject to a tree-expansion criterion (114.2);
a reward is assigned (114.6) to a child node if the transport mission is fulfilled; and
an anticipated value of the assigned reward is added (114.8) to all parent nodes.-
公开(公告)号:EP3582144B1
公开(公告)日:2024-11-13
申请号:EP18750641.5
申请日:2018-02-02
发明人: HUANG, Shengjun , GAO, Nengneng , YUAN, Kun , CHEN, Wei , WANG, Di
IPC分类号: G06N7/01 , G06N20/00 , G06F18/22 , G06F18/25 , G06N5/01 , G06V10/772 , G06V10/778 , G06V10/80 , G06V10/774 , G06V10/74
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公开(公告)号:EP4459512A2
公开(公告)日:2024-11-06
申请号:EP24201171.6
申请日:2019-06-05
IPC分类号: G06N5/01
摘要: Disclosed herein are example layouts and layout generation techniques for fault-tolerant quantum computers. Example embodiments comprise methods for performing a layout reduction technique for fault-tolerant quantum computing. In certain embodiments, a layout of an arbitrary quantum circuit is reduced to a layout of exponents of a multiple qubit Pauli matrix and measurements of a multiple qubit Pauli matrix. In certain embodiments, qubits are marked as one of a data, interface, or ancilla qubit for a 2D nearest neighbor graph of qubit connectivity, and an ancilla-path is provided from a respective data qubit to a respective interface qubit
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公开(公告)号:EP4455716A1
公开(公告)日:2024-10-30
申请号:EP23169752.5
申请日:2023-04-25
发明人: Hain, Stefan , Gromer, Jakob , Pelzl, Tim
摘要: A method for generating a classifier (460) for a target candidate included in target report data (422) generated from radar measurements is disclosed. The method comprises: recording (S110) the target report data (422); selecting (S120) training data from the target report data (422); and generating (S130) a classification model (300) based on the selected training data, the classification model (300) is adapted to decide whether the target candidate is a true target (380) or a false target (390).
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公开(公告)号:EP4400966A3
公开(公告)日:2024-10-09
申请号:EP24178160.8
申请日:2021-05-27
申请人: Apple Inc.
摘要: Systems and processes for providing, via an electronic device, suggested user actions. The suggested actions are provided in response to detecting an occurrence of a predefined event occurring in the user's day. The occurrence of the anchor is encoded in signals generated by the electronic device. The occurrence of the anchor is detectable via monitoring and analysis of electronic signals. Based on the user's previous interactions with the device, the occurrence of the anchor is indicative of user behavior and/or action taken in response to the anchor. Machine learning (ML) is employed to train an anchor model to associate actions taken in response to anchor occurrences. The trained anchor model is employed to detect anchors and provide suggested actions in response to the detected anchor occurrence. The suggested action is based on a type of anchor occurrence and contextual conditions of the anchor occurrences.
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公开(公告)号:EP4390773A1
公开(公告)日:2024-06-26
申请号:EP22214556.7
申请日:2022-12-19
摘要: A mechanism for determining the reliability of a machine-learning model. A first processing system processing input and/or output data for the machine-learning model to predict a reliability of the model. If the reliability meets one or more predetermined criteria, the input and/or output data is passed to a second processing device that also predicts a reliability of the model. The prediction process used by the second processing device is more resource-intensive than the prediction process used by the first processing device.
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