Abstract: The present disclosure relates to autonomous driving technology, and provides a vehicle control method for platooning, a vehicle mounted apparatus, and a vehicle.

Abstract: The present disclosure discloses a method, apparatus, medium, and device for vehicle automatic navigation control.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: An array substrate, includes: a substrate, a first metal layer, a first buffer layer, and an active layer, a gate insulating layer, a second metal layer, a first insulating layer, a third metal layer and a first planarization layer. The first metal layer is electrically connected with the first doped area of the active layer through the bridge layer of the second metal layer. The third metal layer is electrically connected with the second doped area of the active layer. The array substrate of the present disclosure reduces a size of a thin film transistor by stacking the first metal layer, the second metal layer, and the third metal layer, thereby increasing pixel density. A display panel is also provided.

Abstract: Methods of treating or reducing the risk of obesity and/or obesity-related disorders, e.g., metabolic syndrome, hepatic and non-hepatic steatosis, and diabetes, using C20orf27 proteins or nucleic acids.

Abstract: The present invention provides a pose estimation method for an unmanned aerial vehicle based on point, line and plane feature fusion, the method comprises the following steps: S1 extracting an RGB image and a depth map by using an RGB-D camera; S2 constructing a plane parameter space (PPS) according to the depth map, and mapping the depth map from a Cartesian space to the plane parameter space (PPS); S3 constructing an inverse octree structure, comprising: reversely constructing cell nodes for the plane parameter space (PPS) according to an octree rule, and for each cell node, performing fitting by using Gaussian distribution; S4 extracting a plane feature, comprising: extracting the plane feature on the basis of degree of feature extraction algorithm of the inverse octree; S5 extracting a linear feature, comprising: extracting a linear feature on the RGB image by using LSD algorithm detection.

Abstract: Systems and techniques for multi-factor cloud service storage device error prediction are described herein. A set of storage device metrics and a set of computing system metrics may be obtained. A feature set may be generated using the set of storage device metrics and the set of computing system metrics. Members of the feature set may be validated by evaluating a validation training dataset using the members of the feature set. A modified feature set may be created based on the validation. A storage device failure model may be created using the modified feature set. A storage device rating range may be determined by minimizing a cost of misclassification of a storage device. A set of storage devices to be labeled may be identified as having a high probability of failure.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: A touch array substrate and a manufacturing method thereof, wherein in the touch array substrate, an active layer, an insulating layer, a pixel electrode layer, a metal layer, a planarization layer, and a common electrode layer are sequentially disposed on the buffer layer. The active layer includes a first region corresponding to a source electrode and a second region corresponding to a drain electrode. The pixel electrode layer includes a plurality of base layers. The metal layer is correspondingly disposed on the base layers. The metal layer includes a touch signal line, a data line, and a gate electrode. The common electrode layer includes a touch electrode, the source electrode, and the drain electrode.

Abstract: A wearable mechano-acoustic sensor for continuous cardiorespiratory monitoring, and methods of making and using the same. The sensor includes a diaphragm with a chamber and a channel connected to the chamber, a plurality of electrodes including at least an anode and a cathode extending into the channel, and a liquid electrolyte solution that fills the chamber and channel. When the diaphragm is attached to a user's chest, mechano-acoustic movement from the chest cause the diaphragm to move, pushing the electrolyte solution across the electrodes. A voltage is applied to the anode and an electrochemical current is determined by the flux from the anode to the cathode by the modulation of the electrolyte solution across the electrodes and cardiorespiratory signals are measured from the electrochemical currents.

Abstract: The present application relates to a benzo five-membered nitrogen heterocyclic compound and an application thereof. The benzo five-membered nitrogen heterocyclic compound has the structure represented by formula I. The compound can be used as a compound of ROR? receptor inhibitor. The compound can effectively inhibit ROR? proteins and have good selectivity to other nuclear receptor family proteins. The benzo five-membered nitrogen heterocyclic compound or a pharmaceutical composition thereof provided by the present application can be used for preparing a drug for treating, preventing or ameliorating diseases such as inflammations, autoimmune diseases, cell proliferative disorder diseases, sepsis, cancer, neurodegeneration diseases or viral infections, has a good inhibitory effect on the treatment of tumors, especially the treatment of prostate cancer, and also has an amelioration effect on the treatment of other diseases.

Abstract: A method, computer program, and computer system for separating a target voice from among a plurality of speakers is provided. Video data associated with the plurality of speakers and audio data associated with each of the one or more speakers are received. Video feature data is extracted from the received video data. The target voice is identified from among the plurality of speakers based on the received audio data and the extracted video feature data.

Abstract: A vehicle control method for platooning vehicles includes determining, for a first following vehicle, a predicted movement state information, and a lead vehicle predicted movement state information of a lead vehicle corresponding to a number of time points in the future. In the vehicle control method, when there is an adjacent second following vehicle, second following vehicle predicted movement state information corresponding to the number of time points is determined, and also includes determining optimized control quantities corresponding to respective ones of the number of time points, including determining an overall constraint model based on constraints between the first following vehicle and the lead vehicle, and constraints between the first following vehicle and the second following vehicle; determining the optimized control quantities; and performing longitudinal control in accordance with the optimized control quantities.

Abstract: The present invention discloses a cloud-edge-end cooperative control method of a 5G networked UAV for security rescue, including: an image acquisition step: performing, by a single-chip microcomputer, attitude resolution on data acquired by a detection sensor, to obtain image data; a sparse landmark map building step: performing, by a control platform, front-end feature point matching, local map building and optimization, loopback detection, and frame resolution on the image data, to generate a sparse landmark map; a three-dimensional dense map building step: generating, by an edge cloud, a three-dimensional dense map based on a key frame pose and key frame observation data of the sparse landmark map; a high-precision semantic map building step: obtaining a high-precision semantic map; and a UAV movement step: adjusting, by the driving mechanism, a pose of the UAV according to the three-dimensional dense map or the high-precision semantic map.

Abstract: There is included a method and apparatus comprising computer code for generating enhanced target speech from audio data, performed by a computing device, the method comprising: receiving audio data corresponding to one or more speakers; generating estimated an target speech, an estimated noise, and an estimated echo simultaneously based on the audio data using a jointly trained complex ratio mask; predicting frame-level multi-tap time-frequency (T-F) spatio-temporal-echo filter weights based on the estimated target speech, the estimated noise, and the estimated echo using a trained neural network model; and predicting enhanced target speech based on the frame-level multi-tap T-F spatio-temporal-echo filter weights.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Abstract: The present application provides an X-ray imaging system and a collision prevention method therefor. The X-ray imaging system includes a tube device, at least one sensor, and a controller. The tube device is capable of moving to a preset position so as to emit an X-ray. The at least one sensor is mounted on at least one surface of the tube device. The at least one sensor is configured to acquire a feedback signal within a preset sensing region. The controller is connected to the at least one sensor and the tube device, and the controller includes a determination module and a control module. The determination module is configured to determine, on the basis of the feedback signal, whether an obstacle is present in the preset sensing region and acquire position information of the obstacle. The control module is configured to control motion of the tube device on the basis of the position information of the obstacle.

Abstract: A landing tracking control method comprises the following contents: a tracking model training stage and an unmanned aerial vehicle real-time tracking stage. The landing tracking control method extracts a network Snet by using a lightweight feature and makes modification, so that an extraction speed of the feature is increased to better meet a real-time requirement. Weight allocation on the importance of channel information is carried out to differentiate effective features more purposefully and utilize the features, so that the tracking precision is improved. In order to improve a training effect of the network, a loss function of an RPN network is optimized, a regression precision of a target frame is measured by using CIOU, and meanwhile, calculation of classified loss function is adjusted according to CIOU, and a relation between a regression network and classification network is enhanced.