Abstract: An inrush current detection method, an inrush current detection device and a computer-readable storage medium for a transformer are disclosed. The inrush current detection method includes sampling at least a part of a current signal of the transformer to obtain a numerical matrix; providing the numerical matrix as an input to an inrush current detection neural network; and calculating and outputting a label vector corresponding to the numerical matrix by the inrush current detection neural network, wherein the label vector indicates whether the current signal is an inrush current.

Abstract: An optical device includes an imaging system provided to generate a plurality of images being distributed over an imaging plane. It further includes a micro-lens-array, MLA, wherein micro-lenses are assigned to individual images and are provided to form channels of light beams by projecting the individual images towards a projecting plane. An optical expander is arranged between the MLA and the projecting plane and includes an input surface and least two output surfaces. The optical expander is provided to fan out and direct the channels of light beams onto the projecting plane, such that individual projections of the individual images are formed on the projection plane. An overall projection is formed on the projection plane by superimposing the individual projections.

Abstract: According to some embodiments, systems, methods and media for dynamically generating scenario parameters for an autonomous driving vehicles (ADV) are described. In one embodiment, when an ADV enters a driving scenario, the ADV can invoke a map-based scenario checker to determine the type of scenario, and invokes a corresponding neural network model to generate a set of parameters for the scenario based on real-time environmental conditions (e.g., traffics) and vehicle status information (e.g., speed). The set of scenario parameters can be a set of extra constraints for configuring the ADV to drive in a driving mode corresponding to the scenario.

Abstract: Methods are disclosed that enable a user equipment (UE) to receive cell specific, UE specific and, in some embodiments, Physical Uplink Shared Channel (PUSCH) specific parameters from a network side component, such as a transmit/receive point, and use that information to set at least one PUSCH transmit power when transmitting to the network side.

Abstract: The present disclosure discloses a tibial plateau prosthesis with trabeculae containing zirconium-niobium alloy on oxidation layer and a preparation method thereof.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: The present disclosure discloses a zonal trabecular uni-compartmental femoral condylar component containing zirconium-niobium alloy on oxidation layer and preparation method, including following steps: using zirconium niobium alloy powder as raw material, conducting a 3D printing for one-piece molding to obtain an intermediate product of the uni-compartmental femoral condylar component, performing hot isostatic pressing and cryogenic oxidation to obtain the uni-compartmental femoral condylar component; the uni-compartmental femoral condylar component includes an articular surface and an osseointegration surface, a bone trabeculae is arranged on the osseointegration surface.

Abstract: The present invention discloses a zonal trabecular uni-compartmental tibial plateau containing zirconium-niobium alloy on oxidation layer and preparation method, including following steps: using zirconium niobium alloy powder as raw material, conducting a 3D printing for one-piece molding to obtain an intermediate product of the uni-compartmental tibial plateau, performing hot isostatic pressing and cryogenic oxidation to obtain the uni-compartmental tibial plateau; the lower surface of the semi-tibial plateau support and the surface of the keel plate are both provided with a bone trabeculae; the zonal trabecular uni-compartmental tibial plateau adopts the structure of arranging step distributed bone trabeculaes which can reduce the fretting wear of the interface between the prosthesis and the bone, and reduce the stress shielding effect of the prosthesis on the bone tissue, homogenize the stress of the tibial plateau bone tissue, and improve the initial stability and long-term stability of the uni-compartmen

Abstract: Methods and systems are provided in which a UE transmits sidelink (SL) control information comprising a scheduling assignment (SA) indicating an SL transmission resource for a SL data transmission to a target UE. The SL transmission resource is based on one or more occupied SL resources identified by the UE and the SL transmission resource includes resources for an initial transmission and one or more retransmissions. The UE then transmits the SL data transmission using the SL transmission resource according to the transmitted SL control information. This is done without receiving, in a downlink control information (DCI), a grant of communication resources.

Abstract: Method and apparatus for wheel speed estimation include an electrical powertrain having an electric motor providing a motor speed, a wheel, and a mechanical coupling between the motor and the wheel, and an electronic control unit calculating an estimated wheel speed based on the motor speed and mechanical dynamic models of the electrical powertrain.

Abstract: Systems and methods for generating labelled audio data and onboard validation of the labelled audio data utilizing an autonomous driving vehicle (ADV) while the ADV is operating within a driving environment are disclosed. The method includes recording a sound emitted by an object within the driving environment of the ADV, and converting the recorded sound into audio samples. The method further includes labelling the audio samples, and refining the labelled audio samples to produce refined labelled audio data. The refined labelled audio data is utilized to subsequently train a machine learning algorithm to recognize a sound source during autonomous driving of the ADV. The method further includes generating a performance profile of the refined labelled audio data based at least on the audio samples, a position of the object, and a relative direction of the object. The position of the object and the relative direction of the object are determined by a perception system of the ADV.

Abstract: Systems and methods are disclosed for optimizing values of a set of tunable parameters of an autonomous driving vehicle (ADV). The controllers can be a linear quadratic regular, a “bicycle model,” a model-reference adaptive controller (MRAC) that reduces actuation latency in control subsystems such as steering, braking, and throttle, or other controller (“controllers”). An optimizer selects a set tunable parameters for the controllers. A task distribution system pairs each set of parameters with each of a plurality of simulated driving scenarios, and dispatches a task to the simulator to perform the simulation with the set of parameters. Each simulation is scored. A weighted score is generated from the simulation. The optimizer uses the weighted score as a target objective for a next iteration of the optimizer, for a fixed number of iterations. A physical real-world ADV is navigated using the optimized set of parameters for the controllers in the ADV.

Abstract: According to various embodiments, described herein is a method of creating a simulation environment with multiple simulation obstacle vehicles, each with a different human-like driving style. Training datasets with different driving styles can be collected from individual human drivers, and can be combined to generate mixed datasets, each mixed dataset including only data of a particular driving style. Multiple learning-based motion planner critics can be trained using the mixed datasets, and can be used to tune multiple motion planners. Each tuned motion planner can have a different human-like driving style, and can be installed in one of multiple simulation obstacle vehicles. The simulation obstacle vehicles with different human-like driving styles can be deployed to the simulation environment to make the simulation environment more resemble a real-world driving environment.

Abstract: According to some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.

Abstract: An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

Abstract: An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. Multiple trajectories of the obstacle are predicted with corresponding probabilities including a first predicted trajectory of the obstacle with a highest probability and a second predicted trajectory of the obstacle with a second highest probability. A cautionary trajectory of the ADV is planned based on at least one of a difference between the highest probability and the second highest probability or a consequence of the second trajectory. The ADV is to drive with a speed lower than a speed limit and prepare to stop in the cautionary trajectory. The ADV is controlled to drive according to the cautionary trajectory.

Abstract: In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.

Abstract: A user equipment (UE) receives a message that indicates a sidelink (SL) communication resource configuration to be used by the UE for communicating SL control information and SL data between the UE and another UE. The UE transmits SL control information according to the SL communication resource configuration, and transmits SL data according to the SL communication resource configuration. The SL control information and the SL data are transmitted by the UE without the UE receiving, in a downlink control information (DCI), a grant of communication resources.

Abstract: The invention provides a built-in micro-interface oxidation system for preparing terephthalic acid from p-xylene. The oxidation system includes a first reactor, a rectifying tower and a second reactor which are sequentially connected. A first outlet is disposed on a side wall of the first reactor; a first inlet is disposed on a side wall of the second reactor; a material inlet is disposed on a side wall of the rectifying tower; and a material outlet is disposed at a bottom of the rectifying tower. The first outlet is connected with the material inlet of the rectifying tower; the first inlet is connected with the material outlet of the rectifying tower. Micro-interface units are arranged in the first reactor and the second reactor for dispersing and crushing air into bubbles. Through disposing micro-interface units in reactors, problems of high energy consumption, high raw material consumption and low reaction efficiency are solved.

Abstract: Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.