Abstract: The present application discloses a dispatching and control cloud data processing method, device and system. The method includes the following operations: pilot node device acquires a global scheduling task, decomposes the global scheduling task to obtain scheduling tasks, issues the scheduling tasks to collaborative node device, acquires data collection ranges and data processing rules of the collaborative node devices, and delivers them to the collaborative node devices; the collaborative node devices receive and execute the scheduling tasks issued by the pilot node device; receives the data collection ranges and the data processing rules issued by the pilot node device, acquires, based on the scheduling tasks, collected data in the data collection ranges, processes the acquired collected data according to the data processing rules to obtain the processed data, uploads the processed data to the pilot node device; the pilot node device receives the processed data uploaded by the collaborative node devices.

Abstract: The present invention discloses a method and system for recognizing a geometric regularity image of a honeycomb structure. The method includes the steps of image acquisition, image processing, vertex extraction, cell reconstruction, and quality evaluation, wherein a step of binaryzation is set between the step of image processing and the step of vertex extraction, and is to set a pixel value of a background in the image to be 0 and set a pixel value of a honeycomb skeleton in the image to be 1 to form a binary image, and the step of quality evaluation is to calculate angular deviation values of all the cells and an average thereof as well as linear deviation values and an average thereof based on the reconstructed cell image, and determine whether the honeycomb structure is qualified or not by comparing with a set tolerance zone.

Abstract: Disclosed are a method for evaluating a geometric form of a honeycomb product and a system for detecting and evaluating a geometric form of a honeycomb product. The method for evaluating the geometric form includes: acquiring a top-surface image and a side-surface image of the honeycomb product; acquiring vertex coordinates by extracting vertices from the top-surface image; acquiring serial numbers of six vertices of each cell by reconstructing the cells of the top-surface image; computing deviation angles of six interior angles of each cell based on the serial numbers and vertex coordinates of the six vertices of each cell; extracting a top-surface side boundary and a side-surface side boundary of the honeycomb product from the top-surface image and the side-surface image; computing a maximum top-surface deflection and a maximum side-surface deflection of the honeycomb product based on the top-surface side boundary and the side-surface side boundary.

Abstract: A method and apparatus for checking power grid measurement data, a device, a storage medium and a program product. The method includes: a feature factor of power grid measurement data is extracted; power balance of a set time scale is checked based on the measurement data and the feature factor to obtain a check result; a classification rule base of abnormal problems of measurement data is built based on the check result; and an abnormal problem in target measurement data is checked based on the classification rule base of abnormal problems of measurement data, to obtain a measurement data quality report.

Abstract: The disclosure relates to an aerial transportation vehicle including a vehicle frame.

Abstract: Disclosed are a method for evaluating a geometric form of a honeycomb product and a system for detecting and evaluating a geometric form of a honeycomb product. The method for evaluating the geometric form includes: acquiring a top-surface image and a side-surface image of the honeycomb product; acquiring vertex coordinates by extracting vertices from the top-surface image; acquiring serial numbers of six vertices of each cell by reconstructing the cells of the top-surface image; computing deviation angles of six interior angles of each cell based on the serial numbers and vertex coordinates of the six vertices of each cell; extracting a top-surface side boundary and a side-surface side boundary of the honeycomb product from the top-surface image and the side-surface image; computing a maximum top-surface deflection and a maximum side-surface deflection of the honeycomb product based on the top-surface side boundary and the side-surface side boundary.

Abstract: The present invention discloses a method and system for recognizing a geometric regularity image of a honeycomb structure. The method includes the steps of image acquisition, image processing, vertex extraction, cell reconstruction, and quality evaluation, wherein a step of binaryzation is set between the step of image processing and the step of vertex extraction, and is to set a pixel value of a background in the image to be 0 and set a pixel value of a honeycomb skeleton in the image to be 1 to form a binary image, and the step of quality evaluation is to calculate angular deviation values of all the cells and an average thereof as well as linear deviation values and an average thereof based on the reconstructed cell image, and determine whether the honeycomb structure is qualified or not by comparing with a set tolerance zone.

Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.

Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include running an autonomous vehicle that performs right-of-way movements relative to agents, recording, for a plurality of segments of time or distance, where the autonomous vehicle and where the agents are for each tick in each of the plurality of segments, running an autolabeler module on a segment of the plurality of segments to calculate a respective value for each of a plurality of right-of-way labels and using the plurality of right-of-way labels to perform one or more of spoofing an autonomous vehicle stack or to train a right-of-way machine learning model.

Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes obtaining labels associated with various aspects of right-of-way interactions between an autonomous vehicle and an agent, wherein the right-of-way interactions occur when a human driver takes over for the autonomous vehicle and performs the right-of-way interactions, running an autonomous vehicle stack that is untrained for processing right-of-way interactions between the autonomous vehicle and the agent, injecting the labels into the autonomous vehicle stack and determining, based on the injecting of the labels into the autonomous vehicle stack, a performance of the autonomous vehicle stack.

Abstract: Methods and apparatus for identifying features that may have a high potential impact on key application metrics. These methods rely on observational data to estimate the importance of application features, and use causal inference tools such as Double Machine Learning (double ML) or Recurrent Neural Networks (RNN) to estimate the impacts of treatment features on key metrics. These methods may allow developers to estimate the effectiveness of features without running online experiments. These methods may, for example, be used to effectively plan and prioritize online experiments. Results of the online experiments may be used to optimize key metrics of mobile applications, web applications, websites, and other web-based programs.

Abstract: The disclosure relates to an aerial transportation vehicle including a vehicle frame.

Abstract: Systems and methods for the simultaneous localization and mapping of autonomous vehicle systems are provided. A method includes receiving a plurality of input image frames from the plurality of asynchronous image devices triggered at different times to capture the plurality of input image frames. The method includes identifying reference image frame(s) corresponding to a respective input image frame by matching the field of view of the respective input image frame to the fields of view of the reference image frame(s). The method includes determining association(s) between the respective input image frame and three-dimensional map point(s) based on a comparison of the respective input image frame to the one or more reference image frames. The method includes generating an estimated pose for the autonomous vehicle the one or more three-dimensional map points. The method includes updating a continuous-time motion model of the autonomous vehicle based on the estimated pose.

Abstract: In one embodiment, a computing device includes processing circuitry to predict outcomes of unresolved customer support cases. For example, the processing circuitry: receives a request to predict an outcome of an unresolved customer support case; extracts a set of features from a case record for the unresolved case, including a set of categorical features and a set of textual features; encodes the set of categorical and textual features into numerical representations; predicts the outcome of the unresolved customer support case using a trained case prediction model, wherein the trained case prediction model generates a predicted outcome based on the encoded categorical and textual features; and performs a corresponding customer support action based on the predicted outcome of the unresolved customer support case.

Abstract: A method of generating a personalized video for a user of a social networking service. For a plurality of images associated with the user, respective first metrics are determined for respective images of the plurality of images based on one or more predefined image-content criteria, a first set of images is selected from the plurality of images based at least in part on the respective first metrics, and the personalized video is populated with the first set of images. The method further includes providing the personalized video to the user for viewing.

Abstract: A method of generating a personalized video for a user of a social networking service. For a plurality of images associated with the user, respective first metrics are determined for respective images of the plurality of images based on one or more predefined image-content criteria, a first set of images is selected from the plurality of images based at least in part on the respective first metrics, and the personalized video is populated with the first set of images. The method further includes providing the personalized video to the user for viewing.

Abstract: A liquid composite high-intensity sweetener, comprises a sweet substance and a solvent, wherein the sweet substance is one or combination of a plurality of the following components: aspartame, sucralose, acesulfame and neotame; and the solvent comprises water and one or combination of a plurality of the following components: ethanol, propylene glycol, phosphoric acid, citric acid, lactic acid, acetic acid, malic acid, ascorbic acid, glucose acid, tartaric acid, acetate, lactate, citrate, phosphate, sorbic acid, sorbate, benzoic acid and benzoate. In addition, the present invention further claims a method for preparing the liquid composite high-intensity sweetener. The liquid composite high-intensity sweetener not only has sweet taste similar to sucrose and good quality, but also is mixed uniformly without particles remained; the actual requirements of production and consumption are well met.