公开(公告)号：US20240300531A1

公开(公告)日：2024-09-12

申请号：US18654315

申请日：2024-05-03

申请人： Gatik AI Inc.

发明人： Gautam Narang ,  Apeksha Kumavat ,  Arjun Narang ,  Kinh Tieu ,  Michael Smart ,  Marko Ilievski

IPC分类号： B60W60/00 ,  B60W30/09 ,  B60W30/095 ,  G05B13/02 ,  G06N3/045

CPC分类号： B60W60/0011 ,  B60W30/09 ,  B60W30/0956 ,  G05B13/027 ,  G06N3/045 ,  B60W2420/408 ,  B60W2554/20 ,  B60W2554/40 ,  B60W2555/60 ,  B60W2556/50

摘要： A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

公开(公告)号：US20240300032A1

公开(公告)日：2024-09-12

申请号：US18219269

申请日：2023-07-07

申请人： YOUNG’S ENGINEERING CO. LTD.

发明人： Tak Ki YEUNG

IPC分类号： B23B49/00 ,  B23K26/082 ,  B41M5/26 ,  B66F11/04 ,  G05B13/02 ,  G06T7/73 ,  G06V10/40 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776

CPC分类号： B23B49/00 ,  B23K26/082 ,  B41M5/26 ,  B66F11/04 ,  G05B13/027 ,  G06T7/73 ,  G06V10/40 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06T2207/20081

摘要： A printing and drilling system for printing and drilling a target on a building surface that includes a supporting unit for supporting omni-directionally the printing and drilling unit, which has a number of laser position units for illuminating a working area containing the target, an imaging unit to capture images of the working area, a control unit to process the captured images, determine location coordinates of the target, and generate drilling data associated with the target, a laser generator for emitting a laser beam, a laser printing unit movable in x and y axes directions for emitting the laser beam to print the target, and a laser path reflector in the laser printing unit that can focus the laser beam in sequence for printing the target, and an electric drill unit movable from one position to another position for drilling an anchor hole on the laser-marked target.

公开(公告)号：US12072678B2

公开(公告)日：2024-08-27

申请号：US16828343

申请日：2020-03-24

申请人： Honda Motor Co., Ltd.

发明人： Srikanth Malla ,  Chiho Choi

IPC分类号： G05B13/02 ,  B60W60/00 ,  G06V10/25 ,  G06V20/58 ,  G08G1/123

CPC分类号： G05B13/027 ,  G06V10/25 ,  G06V20/58 ,  G08G1/123 ,  B60W60/0027

摘要： In one embodiment, a system includes one or more vehicle sensors for capturing host data and a processor having modules. The data receiving module identifies one or more proximate vehicles within the environment based on one or more of the host data and proximate data received from the one or more proximate vehicles. The motion prediction module generates a first joint uncertainty distribution based on an initial joint uncertainty model and a host model distribution. The motion prediction module also samples host kinematic predictions based on the first joint uncertainty distribution and the host data. The object localization module generates a second joint uncertainty distribution based on the initial joint uncertainty model and an object prediction model distribution. The object localization module also samples proximate kinematic predictions based on the second joint uncertainty distribution and the proximate data.

公开(公告)号：US12040753B2

公开(公告)日：2024-07-16

申请号：US17948482

申请日：2022-09-20

申请人： Analog Devices, Inc.

发明人： Tao Yu ,  Christopher Mayer

IPC分类号： H03F1/32 ,  G05B13/02

CPC分类号： H03F1/3247 ,  G05B13/027

摘要： Systems, devices, and methods related to envelope regulated, digital predistortion (DPD) are provided. An example apparatus includes an envelope regulator circuit to process, based on a parameterized model, an input signal to generate an envelope regulated signal; a digital predistortion (DPD) actuator circuit to process the envelope regulated signal and the input signal based on DPD coefficients associated with a nonlinearity characteristic of a nonlinear component; and a DPD adaptation circuit to update the DPD coefficients based on a feedback signal indicative of an output of the nonlinear component.

公开(公告)号：US12032365B2

公开(公告)日：2024-07-09

申请号：US18357560

申请日：2023-07-24

申请人： Nanotronics Imaging, Inc.

发明人： Matthew C. Putman ,  John B. Putman ,  Vadim Pinskiy ,  Damas Limoge

IPC分类号： G05B13/02 ,  G05B19/418

CPC分类号： G05B19/41875 ,  G05B13/027 ,  G05B2219/32193 ,  G05B2219/32194 ,  G05B2219/32195

摘要： Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.

公开(公告)号：US12032344B2

公开(公告)日：2024-07-09

申请号：US18218983

申请日：2023-07-06

申请人： Tyco Fire & Security GmbH

发明人： Young M. Lee ,  Sugumar Murugesan ,  ZhongYi Jin ,  Jaume Amores

IPC分类号： G05B13/04 ,  F24F11/38 ,  F24F11/63 ,  F24F11/64 ,  G05B13/02 ,  G06N20/00 ,  G06N5/04

CPC分类号： G05B13/048 ,  F24F11/38 ,  F24F11/63 ,  F24F11/64 ,  G05B13/0265 ,  G05B13/04 ,  G06N20/00 ,  G05B13/027 ,  G05B13/028 ,  G06N5/04

摘要： A model management system for building equipment includes one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to determine whether fault data exists in equipment data used to generate a plurality of shutdown prediction models for the building equipment, generate a first performance evaluation value for each of the plurality of shutdown prediction models using a first evaluation technique in response to a determination that the fault data exists in the equipment data, generate a second performance evaluation value for each of the plurality of shutdown prediction models using a second evaluation technique in response to a determination that the fault data does not exist in the equipment data, and select one of the plurality of shutdown prediction models based on the first performance evaluation value and the second performance evaluation value.

公开(公告)号：US12019412B2

公开(公告)日：2024-06-25

申请号：US18214303

申请日：2023-06-26

申请人： THE TRUSTEES OF DARTMOUTH COLLEGE

发明人： Richard Granger ,  Elijah Floyd William Bowen ,  Antonio Rodriguez ,  Andrew Felch

IPC分类号： G05B13/02 ,  G06F7/50 ,  G06F7/523 ,  G06F17/14 ,  G06F17/16 ,  G06V10/44 ,  G06V10/762 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94

CPC分类号： G05B13/027 ,  G06F7/50 ,  G06F7/523 ,  G06F17/141 ,  G06F17/16 ,  G06V10/454 ,  G06V10/7635 ,  G06V10/764 ,  G06V10/82 ,  G06V10/955

摘要： An autonomous module for processing stored data includes a multithreaded processor core (MPC) and a plurality of autonomous memories. Each of the plurality of autonomous memories has a memory bank, a data operator (DO) configured to implement a plurality of selectable memory behaviors, an autonomous memory operator (AMO) configured to implement a state machine to control the memory bank independently of the MPC, and at least one memory input/output (IO) port communicatively coupled with the memory bank, the AMO, and the DO. The at least one memory IO port is configured to receive a read instruction from the AMO, retrieve data from the memory bank, and send the data to the DO. The DO is configured to implement one of the plurality of selectable memory behaviors to update the data and send the updated data to the AMO via the at least one memory IO port.

公开(公告)号：US20240200969A1

公开(公告)日：2024-06-20

申请号：US18067798

申请日：2022-12-19

申请人： QUALCOMM Incorporated

发明人： Volodimir SLOBODYANYUK ,  Radhika Dilip GOWAIKAR ,  Makesh Pravin JOHN WILSON ,  Shantanu Chaisson SANYAL ,  Avdhut JOSHI ,  Christopher BRUNNER ,  Behnaz REZAEI ,  Amin ANSARI

IPC分类号： G01C21/00 ,  G05B13/02 ,  G08G1/01

CPC分类号： G01C21/3807 ,  G01C21/3841 ,  G05B13/027 ,  G08G1/0104

摘要： In some aspects, a device may receive sensor data associated with a vehicle and a set of frames. The device may aggregate, using a first pose, the sensor data associated with the set of frames to generate an aggregated frame, wherein the aggregated frame is associated with a set of cells. The device may obtain an indication of a respective occupancy label for each cell from the set of cells, wherein the respective occupancy label includes a first occupancy label or a second occupancy label, and wherein a subset of cells from the set of cells are associated with the first occupancy label. The device may train, using data associated with the aggregated frame, a machine learning model to generate an occupancy grid, based on a loss function that only calculates a loss for respective cells from the subset of cells. Numerous other aspects are described.

公开(公告)号：US20240191898A1

公开(公告)日：2024-06-13

申请号：US18536538

申请日：2023-12-12

申请人： Computime Ltd.

发明人： Yunjian Xu ,  Liang Liang Hao ,  Shuai Mao ,  Wai-Leung Ha ,  Pak Ming Fan ,  Chi Lung Chan ,  Chi Shing Raymond Wong

IPC分类号： F24F11/63 ,  G05B13/02

CPC分类号： F24F11/63 ,  G05B13/027

摘要： A smart thermostatic system is disclosed that applies one or more of a reinforcement and/or adaptive learning model for a new environment with the trained model from another environment so as to initiate status of a thermostatic device. In one example, the thermostatic system uses a pretrained machine learning model that is transferred from a first thermostatic system to a second thermostatic system in a similar sub-environment. Temperature data and other data collected by the thermostatic device is used to fine-tune and train the pretrained model to learn, predict, and better adjust the operation of the thermostatic system.

公开(公告)号：US11989020B1

公开(公告)日：2024-05-21

申请号：US17125231

申请日：2020-12-17

申请人： Aurora Innovation, Inc.

发明人： James Andrew Bagnell ,  Arun Venkatraman ,  Sanjiban Choudhury

IPC分类号： G05D1/00 ,  G05B13/02 ,  G06N3/084

CPC分类号： G05D1/0088 ,  G05B13/027 ,  G05D1/0221 ,  G06N3/084 ,  G05D2201/0213

摘要： Systems and methods for training a machine learning (“ML”) model for use in controlling an autonomous vehicle (“AV”) are described herein. Implementations can obtain an initial state instance from driving of a vehicle, obtain ground truth label(s) for subsequent state instance(s) that each indicate a corresponding action of the vehicle for a corresponding time instance, perform, for a given time interval, a simulated episode, of locomotion of a simulated AV, generate, for each of a plurality of time instances of the given time interval, subsequent simulated state instance(s) that differ from the subsequent state instance(s), determine, using the ML model, and for each of the time instances, a predicted simulated action of the simulated AV based on the subsequent simulated operation instance(s), generate loss(es) based on the predicted simulated actions and the ground truth labels, and update the ML model based on the loss(es).