Abstract: A power distribution circuit applied to a radio frequency front-end transceiving apparatus includes a common-stage amplifying circuit and a branch-stage amplifying circuit, in which the branch-stage amplifying circuit comprises at least two parallel channel amplifying circuits; the common-stage amplifying circuit is configured to perform a first signal processing on a radio frequency signal received by an antenna of the radio frequency front-end transceiving apparatus to obtain a first power signal and output the first power signal to each of channel amplifying circuits, in which the first signal processing at least includes a buffering processing, an isolation processing and a low-noise amplifying processing; each channel amplifying circuit is configured to perform a second signal processing on the first power signal to obtain a second power signal and output the second power signal to a radio transceiving device; the second signal processing at least includes a low-noise amplifying processing.

Abstract: Body and hand correlation method, apparatus, device, storage medium and computer program are provided. Method includes: in an image of which an image content includes a body to be correlated and a hand to be correlated, a first correlation probability between a body detection box of the body and a hand detection box of the hand is determined; a second correlation probability between the body and a wrist key point in a key point is determined based on the key point in the body detection box, the key point including the wrist key point and elbow key point belonging to the same arm; a third correlation probability between the hand detection box and the wrist key point is determined based on the wrist key point and elbow key point; and a correlation degree between the body and the hand is determined based on the first, second, and third correlation probabilities.

Abstract: A method and device for identifying a state, electronic device and computer-readable storage medium are provided. The method includes: acquiring a to-be-detected image for a specific scene and determining a region of interest in the to-be-detected image; the region of interest being a region obtained by subtracting an occlusion range of the storage container in a closed state from an occlusion range in an open state; determining a positional relation between the region of interest and specific object regions where the specific objects are positioned in the to-be-detected image; determining at least one value based on values of pixels in the region of interest, when the positional relation represents that there is no specific object region partially overlapping the region of interest; and determining whether the storage container is in an open state or closed state based on the at least one value and a preset value range.

Abstract: Methods, systems, and apparatus for detecting correlated objects involved in images are provided. In one aspect, a method includes: detecting a face object, a preset body part object, and a hand object involved in an image, performing a respective correlation prediction on every two of the face object, the preset body part object, and the hand object to obtain first, second, and third correlation prediction results, segmenting the image to determine at least one body object involved in the image to determine a first body object to which the face object belongs and a second body object to which the preset body part object belongs, adjusting the first correlation prediction result based on the first body object and the second body object, and determining correlated objects involved in the image according to the adjusted first correlation prediction result and the second and third correlation prediction results.

Abstract: A tight connection device for the aseptic transfer of a biopharmaceutical product includes: a stationary temporary clamping means keeping the container hermetically clamped against the chamber; a stationary unlocking means capable of switching the container from an initial locked position to an intermediate unlocked position; a stationary locking means; and an annular functional crown capable of being rotated to actuate the stationary unlocking means and the stationary locking means of the container. The stationary unlocking means and the stationary locking means are mechanically linked to the annular functional crown and arranged such that the rotation of the annular functional crown and the end locked position. The device further includes stationary immobilising/release means capable of allowing and preventing the annular functional crown to rotate.

Abstract: A body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

Abstract: An object recognition method, apparatus, device and storage medium are provided. The method includes the following steps: a real-time image where a screen includes an object operation region is acquired; image reference points that match with at least two reference origins in the real-time image is determined according to the mapping relationship between the real-time image and a preset standard image, the reference origin is a point within the object operation region in the preset standard image; and an object within the object operation region is recognized according to a pixel coordinate of the preset standard image and a reference pixel coordinate of the image reference point in the real-time image to obtain a target recognition result of the object.

Abstract: Embodiments of the present disclosure disclose forward collision control methods and apparatuses, electronic devices, programs, and media, where the forward collision control method includes: detecting a forward suspected collision object on a road where a current traveling object is located on the basis of a neural network; predicting the collision time between the current traveling object and the suspected collision object; and performing forward collision control on the current traveling object according to the collision time, where the forward collision control includes forward collision warning and/or driving control.

Abstract: A bag for containing a biopharmaceutical product includes at least one fluid input/output port and a measurement opening remote from the fluid port. A probe holder port has a body with an axial through-bore and a peripheral collar with a second fastening zone and defining a fastening plane. A contact probe, extending in the axial through-bore, is assembled on the probe holder port, has an active measurement end portion in contact with the product, and has an end portion opposite the end portion. The probe holder port includes a transition portion between the body and the collar, which extends between a radially central end portion of the collar and a first axial end portion of the body. The first axial end portion is the axial end portion closest to the radially central end portion of the collar. The first axial end portion is disposed on the outer side relative to the fastening plane, and the active measurement end portion is axially disposed on the outer side relative to the fastening plane.

Abstract: The present disclosure provides a method, apparatus and system for identifying target objects. The method includes: clipping out a target image from an acquired image, wherein the target image involves a plurality of target objects to be identified that are stacked; adjusting a height of the target image to a preset height, wherein a height direction of the target image corresponds to a direction in which the plurality of target objects are stacked; extracting a feature map of the adjusted target image; segmenting the feature map in a dimension corresponding to the height direction of the target image to obtain a preset number of segment features; and identifying the target objects based on each of the preset number of segment features.

Abstract: An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

Abstract: Various embodiments of the teachings herein include a mixer disposed in a fuel gas combustion system and mixing air and fuel gas to form flammable mixed gases. The mixer may include: a Venturi tube having an air inlet, a fuel gas inlet, a gas mixture outlet, a central axis direction, and a throat positioned between the air inlet and the gas mixture outlet in the central axis direction, wherein the fuel gas inlet is disposed at the throat; and an adjustment component disposed in the Venturi tube downstream of the throat, the adjustment component drivable to move towards or away from the throat in the central axis direction, thereby changing a flow area of gas in the Venturi tube. The adjustment component comprises a conical valve plug with a conical outer surface thereof at a side facing towards the throat and fitting an inner surface of the Venturi tube.

Abstract: Provided are a training and detection method and apparatus of an object detection network and a device and a storage medium. The method of training an object detection network includes: obtaining, by performing object detection for images in an image data set input into the object detection network and for each of one or more objects involved in each of the images, a confidence levels that the object is predicted as each of a plurality of preset categories; for each of the objects, determining reference labeling information of the object with respect to each of the non-labeled categories; for each of the objects, determining loss information that the object is predicted as each of the preset categories; and adjusting a network parameter of the object detection network based on the loss information that each of the objects is predicted as each of the preset categories.

Abstract: A body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

Abstract: Body and hand correlation method, apparatus, device, storage medium and computer program are provided. Method includes: in an image of which an image content includes a body to be correlated and a hand to be correlated, a first correlation probability between a body detection box of the body and a hand detection box of the hand is determined; a second correlation probability between the body and a wrist key point in a key point is determined based on the key point in the body detection box, the key point including the wrist key point and elbow key point belonging to the same arm; a third correlation probability between the hand detection box and the wrist key point is determined based on the wrist key point and elbow key point; and a correlation degree between the body and the hand is determined based on the first, second, and third correlation probabilities.

Abstract: The embodiments provide an image data generation method, which includes that: multiple virtual three-dimensional prop models respectively corresponding to multiple kinds of table game props are acquired, the table game prop being a game tool used in a table game scene; in a virtual three-dimensional table game scene, at least one virtual three-dimensional prop model including at least one kind of table game prop is randomly determined from the multiple virtual three-dimensional prop models; the at least one virtual three-dimensional prop model is overlaid to the virtual three-dimensional table game scene to form a virtual target game scene; and planar projection processing is performed on the virtual target game scene to obtain two-dimensional image data including the at least one kind of table game prop. The embodiments also disclose an image data generation apparatus, an electronic device, and a computer-readable storage medium.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; splitting the first feature map into a plurality of first sub-feature maps according to dimension information of the first feature map and a preset splitting rule, wherein the dimension information of the first feature map comprises dimensions of the first feature map and size of each dimension; performing normalization on the plurality of first sub-feature maps respectively to obtain a plurality of second sub-feature maps; and splicing the plurality of second sub-feature maps to obtain a second feature map of the image to be processed. Embodiments of the present disclosure can reduce the statistical errors during normalization of a complete feature map.

Abstract: Embodiments of the present disclosure provide a method and apparatus for point cloud completion, a network training method and apparatus, a device, and a storage medium. The method includes: determining a probability distribution of an acquired first point cloud; completing the first point cloud based on the probability distribution to obtain a primary completed point cloud; concatenating the primary completed point cloud and the first point cloud to obtain a concatenated point cloud; determining association relationships between the concatenated point cloud and multiple groups of neighbouring points of the concatenated point cloud; completing the concatenated point cloud based on the association relationships to obtain a second point cloud from completion the first point cloud.

Abstract: Disclosed is a method and apparatus for point cloud data processing, an electronic device and a computer storage medium. For each of multiple data points of first point cloud data, initial feature information of a respective one of the multiple data points and initial feature information of each of multiple neighboring points of the respective data point taken as a center point are acquired; correlation degree information between the respective data point and the multiple neighboring points is determined based on the initial feature information of the respective data point and the initial feature information of each of the multiple neighboring points; first target feature information of the respective data point is determined based on the correlation degree information; and point cloud data reconstruction is performed based on the first target feature information of each of the multiple data points to obtain second point cloud data.

Abstract: A method and apparatus for processing point cloud data, a device, and a storage medium are provided. The method includes: determining, from first point cloud data acquired, multiple groups of neighbouring points for a to-be-processed point, wherein each group of neighbouring points among the multiple groups of neighbouring points has a respective different scale; for each group of neighbouring points, determining a respective association relationship between the group of neighbouring points and the to-be-processed point; for each group of neighbouring points, determining a respective association feature of the to-be-processed point based on the respective association relationship; determining a target feature of the to-be-processed point based on association features corresponding to the multiple groups of neighbouring points; and performing, based on target features of multiple to-be-processed points, point cloud completion on the first point cloud data to generate second point cloud data.