Abstract: Disclose are a roadway/tunnel excavation robot and an automatic cutting control method. The robot includes a rack, a moving platform, a supporting and stabilizing mechanism, a milling mechanism, a telescoping mechanism, an inclined cutting feed adjusting mechanism, a horizontal swinging mechanism, a lifting mechanism and a controller. The milling mechanism includes a drive unit, a milling shaft, an eccentric rotary casing, a high-pressure jet nozzle unit, a tension and compression sensor and a direction sensor. Through the deflection of a center line of an inner hole of the eccentric rotary casing, the milling mechanism drives a milling cutter head to carry out a rotational oscillation motion for rock breaking; and the milling cutter head is in discontinuous contact with a rock mass. The telescoping mechanism, the inclined cutting feed adjusting mechanism, the lifting mechanism and the horizontal swinging mechanism are controlled such that the milling mechanism performs coal rocks milling.

Abstract: The present disclosure discloses a method for execution of a computational graph in a neural network model and an apparatus thereof, including: creating task execution bodies on a native machine according to a physical computational graph compiled and generated by a deep learning framework, and designing a solution for allocating a plurality of idle memory blocks to each task execution body, so that the entire computational graph participates in deep learning training tasks of different batches of data in a pipelining and parallelizing manner.

Abstract: The present invention discloses a hydrate energy-storage temperature-control material and a preparation method therefor. The material includes a refrigerant hydrate and a cross-linked polymer. The preparation method comprises the following steps: first, preparing a refrigerant hydrate by using a high-pressure reactor, and conducting grinding, crushing and sieving to obtain hydrate particles; then, uniformly spraying polytetrafluoroethylene suspended ultrafine powder onto the surface of the hydrate particles by using an electrostatic spraying device, and putting the hydrate particles into a plasma instrument to modify polytetrafluoroethylene so as to allow free radicals to be formed on the polytetrafluoroethylene powder surface; finally, subjecting monomers to graft polymerization with the free radicals on the polytetrafluoroethylene surface under the irradiation of a high-pressure mercury lamp of UV lighting system to stabilize the structure of the material, preparing a final product.

Abstract: A system is described for redirecting multimedia in a collaborative session on a virtual desktop. The virtual desktop session can be established, and collaborator virtual desktop clients can be connected in a collaborative session where each collaborator can view the desktop GUI in their respective virtual desktop client. A request can be received to play media in a media player in the virtual desktop. The media stream can be intercepted in the virtual desktop before it is rendered in the media player and conveyed to each collaborator's client over a separate virtual channel established between the virtual desktop and each collaborator. The data stream can then be rendered in a client media player by each collaborator's client.

Abstract: A method enables a user to bypass a pass code, such as personal identification number (PIN), when performing an authentication process between a client device and a gateway/broker of a virtual desktop infrastructure (VDI) provided by a virtualized computing environment. A customized operating system (OS) of the client device includes pre-installed certificates and keys that are used in the authentication process and which are protected by the PIN. Through a public interface, a client device may perform the authentication process without requiring the user to manually enter the PIN.

Abstract: The disclosure provides an image processing method and device, a computer-readable storage medium. The method includes: performing feature extraction on an image to be processed through a primary image segmentation model to obtain a feature map of the image to be processed; and performing image segmentation on a segmentation target according to the feature map through a target image segmentation model to obtain a target segmentation result, herein the target image segmentation model is obtained from an adversarial training with an image encoding model, and the image encoding model is used for placing a shape constraint on the target segmentation result. The disclosure may improve an accuracy of the image segmentation.

Abstract: In some embodiments, a control system, a control method and a storage medium are provided. In the method, first motion information of a machine acquired by a first sensor is received; the first motion information is inputted into a deep learning model to obtain a model output, the deep learning model comprising a convolutional neural network (CNN) and a long short-term memory (LSTM); the deep learning model is trained using the first motion information and second motion information acquired by a second sensor; the first sensor and the second sensor having different ways of detecting information and processing the detected information. The model output is used to control the machine.

Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device and a storage medium. The method includes performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed and performing weight prediction on the first feature map to obtain a weight feature map of the first feature map. The weight feature map includes weight values of feature points in the first feature map. The method further includes performing feature value adjustment on the feature points in the first feature map based on the weight feature map to obtain a second feature map and determining a processing result of the image to be processed according to the second feature map. Embodiments of the present disclosure may improve the image processing accuracy.

Abstract: A method and apparatus for positioning a description statement in an image includes: analyzing a to-be-analyzed description statement and a to-be-analyzed image to obtain a plurality of statement attention weights of the to-be-analyzed description statement and a plurality of image attention weights of the to-be-analyzed image; obtaining a plurality of first matching scores based on the plurality of statement attention weights and a subject feature, a location feature and a relationship feature of the to-be-analyzed image; obtaining a second matching score between the to-be-analyzed description statement and the to-be-analyzed image based on the plurality of first matching scores and the plurality of image attention weights; and determining a positioning result of the to-be-analyzed description statement in the to-be-analyzed image based on the second matching score.

Abstract: The present disclosure discloses an image processing method and related device thereof. The method includes: acquiring an image to be processed; and performing a feature extraction process on the image to be processed using a target neural network so as to obtain target feature data of the image to be processed, wherein parameters of the target neural network are time average values of parameters of a first neural network which is obtained from training under supervision by a training image set and an average network, and parameters of the average network are time average values of parameters of a second neural network which is obtained from training under supervision by the training image set and the target neural network. A corresponding device is also disclosed. Feature data of image to be processed are obtained via the feature extraction process performed on the image to be processed.

Abstract: The present disclosure relates to a network optimization method and apparatus, an image processing method and apparatus, and a storage medium. The network optimization method includes: obtaining an image sample group; obtaining a first feature and a second feature of an image in the image sample group, and obtaining a first classification result by using the first feature of the image; performing feature exchange processing on an image pair in the image sample group to obtain a new image pair; obtaining a first loss value of the first classification result, a second loss value of the new image pair, and a third loss value of first features and second features of the new image pair in a preset manner; and adjusting parameters of a neural network at least according to the first loss value, the second loss value, and the third loss value until a preset requirement is met.

Abstract: The present disclosure relates to an image retrieval method and apparatus. The method includes: obtaining an image group including a query image and at least one candidate image; calculating an intermediate similarity between any two images in the image group; performing multi-scale feature fusion based on the intermediate similarity between the any two images to obtain a final similarity between the query image and each candidate image; and determining a target image corresponding to the query image from each candidate image based on the final similarity. The present disclosure can improve precision and robustness of image retrieval.

Abstract: Methods, apparatuses, devices, and computer-readable storage media for three-dimensional object detection and intelligent driving are provided.

Abstract: A pedestrian re-identification method includes: obtaining a target video containing a target pedestrian and at least one candidate video; encoding each target video segment in the target video and each candidate video segment in the at least one candidate segment separately; determining a score of similarity between the each target video segment and the each candidate video segment according to encoding results, the score of similarity being used for representing a degree of similarity between pedestrian features in the target video segment and the candidate video segment; and performing pedestrian re-identification on the at least one candidate video according to the score of similarity.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: The disclosure provides an image processing method and device, a computer-readable storage medium. The method includes: performing feature extraction on an image to be processed through a primary image segmentation model to obtain a feature map of the image to be processed; and performing image segmentation on a segmentation target according to the feature map through a target image segmentation model to obtain a target segmentation result, herein the target image segmentation model is obtained from an adversarial training with an image encoding model, and the image encoding model is used for placing a shape constraint on the target segmentation result. The disclosure may improve an accuracy of the image segmentation.

Abstract: An image question answering method includes: extracting a question feature representing a semantic meaning of a question, a global feature of an image, and a detection frame feature of a detection frame encircling an object in the image; obtaining a first weight of each of at least one area of the image and a second weight of each of at least one detection frame of the image according to question feature, global feature, and detection frame feature; performing weighting processing on global feature by using first weight to obtain an area attention feature of image; performing weighting processing on detection frame feature by using second weight to obtain a detection frame attention feature of image; and predicting an answer to question according to question feature, area attention feature, and detection frame attention feature.