Beijing Baidu Netcom Science and Technology Co., Ltd.

Tsinghua University

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Fu Li

Hao Sun

Dongliang He

Shilei Wen

Xin Li

Tianwei Lin

Errui Ding

Xiang Long

Zhichao Zhou

Ping Wang

Xiang Zhao

Henan Zhang

Xiao Xi Liu

Zuoyi Zhang

Zhizhen Chi

Yujie Dong

Chao Li

Xin Zhang

Feng Li

Bing Liu

Hongwu Zhang

Xubin Li

Haiping Wang

Xi Zeng

Baoyu Zhang

Rongrong Han

Chenghao Chang

Xinwei Yu

Jiaqi Jiang

Meng Qi

Lixing Gong

Yueyang Song

Fantian Meng

Yu Wang

Yue Li

Feng Xie

Haitao Wang

Jizhou Huang

Bin Wu

Jinhua Wang

Bing Xia

Zongxin Wu

Yukang Ding

Dazhong Wang

Yuanhui Xu

Yuliang Sun

Jianzhu Cao

Jiejuan Tong

Jiong Guo

The present disclosure relates to a method for monitoring failure of coated particles in fuel elements in a core of a pebble-bed high-temperature gas-cooled reactor, which is related to the technical field of nuclear reactor engineering and includes the following steps: S, calculating an inventory of a short-lived noble gas fission nuclide; S, obtaining a ratio of a release rate to a birth rate of the short-lived noble gas fission nuclide based on a temperature of the fuel elements using a Booth diffusion and release model; S, deriving a theoretical expression for an activity concentration of the short-lived noble gas fission nuclide in a primary circuit using a migration model of the nuclide in the primary circuit; S, obtaining an experimental measurement value of the activity concentration of the short-lived noble gas fission nuclide in the primary circuit at a sampling moment by gas sampling; S, optimally calculating a failure fraction of the coated particles in the fuel elements and a share of uranium contamination in the matrix graphite in the core based on the theoretical expression and the experimental measurement value. The present disclosure can provide key parameters for the performance and status of the fuel elements in the core, which are required for radiation safety studies, source term calculations and accident analysis of the pebble-bed high-temperature gas-cooled reactor.

Method for monitoring failure of coated particles in fuel elements in core of pebble-bed high-temperature gas-cooled reactor

Multiple groups of nuclear fuel storage containers can be put into the nuclear fuel transport container provided by the present application as a whole to realize the overall transportation of the multiple groups of storage containers; the transport container has a cubic structure in appearance, which can fully utilize the transportation space and improve the transportation efficiency; an outer side of the transport container is provided with an anti-penetration structure, which can effectively alleviate the collision and penetration damage caused by transportation and hoisting accident, and ensure that the nuclear fuel element can be safely enclosed after the accident; heat insulation material is provided between inner and outer steel plates of the transport container, which can effectively protect the internal storage container and the loaded nuclear fuel element under fire condition, and the heat insulation material also has a function of drop buffer.

Method and equipment for transporting nuclear fuel of nuclear power plant

An image filling method and apparatus, a device and a storage medium are disclosed. The image filling method includes: performing multilevel encoding processing on features of an image to be filled to generate multilevel encoded feature layers, sizes of the multilevel encoded feature layers being reduced layer by layer; performing layer-by-layer decoding processing on the multilevel encoded feature layers to obtain multilevel decoded feature layers and a first image, there being no missing region in the first image, wherein the layer-by-layer decoding processing includes a concatenation operation on a decoded feature layer and an encoded feature layer with a same size; and performing up-sampling processing on the first image to obtain multilevel up-sampled feature layers and a second image optimized by the up-sampling processing, the up-sampling processing including a concatenation operation on an up-sampled feature layer and a decoded feature layer with a same size.

Image filling method and apparatus, device, and storage medium

A meme generation method, an electronic device, and a storage medium are provided. The method includes: determining a plurality of second expression images corresponding to a target face image based on a plurality of first expression images contained in a first meme; generating a second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image; wherein, determining an affine transformation parameter between the target face image and an i-th first expression image in the plurality of first expression images according to a corresponding relation between a face key point in the target face image and a face key point in the i-th first expression image; and transforming the target face image based on the affine transformation parameter to obtain an i-th second expression image corresponding to the target face image.

Meme generation method, electronic device and storage medium

A method and apparatus for processing a video frame are provided. The method may include: converting, using an optical flow generated based on a previous frame and a next frame of adjacent frames in a video, a feature map of the previous frame to obtain a converted feature map; determining, based on an error of the optical flow, a weight of the converted feature map, and obtaining a fused feature map based on a weighted result of a feature of the converted feature map and a feature of a feature map of the next frame; and updating the feature map of the next frame as the fused feature map.

Method and apparatus for processing video frame

The disclosure provides an image processing method, an image processing apparatus, an electronic device and a storage medium, which belongs to the field of computer technologies, and specifically relates to computing vision, image processing, face recognition, and deep learning technologies in artificial intelligence. The method includes: performing skin color recognition on a face image to be processed to determine a target skin color of a face contained in the face image; obtaining a reference transformation image corresponding to the face image by processing the face image using any style transfer model in response that a style transfer model set does not comprise a style transfer model corresponding to the target skin color; and obtaining a target transformation image matching the target skin color by adjusting a hue value, a saturation value, and a lightness value of each pixel in the target region based on the target skin color.

Image processing method, electronic device and storage medium for performing skin color recognition on a face image

Embodiments of the present disclosure provide a method and apparatus for processing an image, and relates to the field of computer vision technology. The method may include: acquiring a value to be processed, where the value to be processed is associated with an image to be processed; and processing the value to be processed by using a quality scoring model to generate a score of the image to be processed in a target scoring domain, where the score of the image to be processed in the target scoring domain is related to an image quality of the image to be processed.

Method and apparatus for processing image

Embodiments of the present disclosure disclose a method and apparatus for searching a video segment, a device and a medium, and relate to the field of video data search. The method includes: sampling video frames from a target video and videos to be searched in a video library, and extracting features from the sampled frames; matching the target video and the videos to be searched according to the extracted features to determine a candidate video to be searched that matches the target video; determining at least one candidate video segment from the determined candidate video, and calculating a degree of matching between the target video and each candidate video segment based on the extracted features of each sampled frame; and determining a video segment matching the target video in the videos to be searched according to the calculated degree of matching between the target video and each candidate video segment.

Method and apparatus for searching video segment, device, and medium

A method includes screening, by a video-clip screening module in a video description model, a plurality of video proposal clips acquired from a video to be analyzed, to acquire a plurality of video clips suitable for description. The plural video proposal clips acquired from the video to be analyzed may be screened by the video-clip screening module to acquire the plural video clips suitable for description; and then, each video clip is described by a video-clip describing module, thus avoiding description of all the video proposal clips, only describing the screened video clips which have strong correlation with the video and are suitable for description, removing the interference of the description of the video clips which are not suitable for description in the description of the video, guaranteeing the accuracy of the final descriptions of the video clips, and improving the quality of the descriptions of the video clips.

Method and apparatus for detecting temporal action of video, electronic device and storage medium

A method and apparatus for detecting a temporal action of a video, an electronic device and a storage medium are disclosed, which relates to the field of video processing technologies. An implementation includes: acquiring an initial temporal feature sequence of a video to be detected; acquiring, by a pre-trained video-temporal-action detecting module, implicit features and explicit features of a plurality of configured temporal anchor boxes based on the initial temporal feature sequence; and acquiring, by the video-temporal-action detecting module, the starting position and the ending position of a video clip containing a specified action, the category of the specified action and the probability that the specified action belongs to the category from the plural temporal anchor boxes according to the explicit features and the implicit features of the plural temporal anchor boxes.

The disclosure discloses a cartoonlization processing method for an image, and relates to a field of computational vision, image processing, face recognition, deep learning technologies. The method includes: performing skin color recognition on a facial image to be processed to determine a target skin color of a face in the facial image; processing the facial image by utilizing any cartoonizing model in a cartoonizing model set to obtain a reference cartoonized image corresponding to the facial image in a case that the cartoonizing model set does not contain a cartoonizing model corresponding to the target skin color; determining a pixel adjustment parameter based on the target skin color and a reference skin color corresponding to the any cartoonizing model; and adjusting a pixel value of each pixel point in the reference cartoonized image based on the pixel adjustment parameter, to obtain a target cartoonized image corresponding to the facial image.

Cartoonlization processing method for image, electronic device, and storage medium

The present disclosure provides a method and apparatus for detecting a region of interest in a video, a device and a storage medium. The method may include: acquiring a current to-be-processed frame from a picture frame sequence of a video; detecting a region of interest (ROI) in the current to-be-processed frame, in response to determining that the current to-be-processed frame is a detection picture frame, to determine at least one ROI in the current to-be-processed frame; and updating a to-be-tracked ROI, based on the ROI in the current to-be-processed frame and a tracking result determined by a pre-order tracking picture frame; and tracking the current to-be-processed frame based on the existing to-be-tracked ROI, in response to determining that the current to-be-processed frame is a tracking picture frame, to determine at least one tracking result as the ROI of the current to-be-processed frame.

Method and apparatus for detecting region of interest in video, device and medium

Embodiments of the present disclosure relate to a method and apparatus for selecting a video clip, a server and a medium. The method may include: determining at least two video clips from a video; for each video clip, perform following excitement determination steps: inputting a feature sequence of a video frame in the video clip and title information of the video into a pre-established prediction model to obtain a relevance between the inputted video frame and a title of the video; and determining an excitement of the video clip, based on the relevance between the video frame in the video clip and the title; and determining a target video clip from the video clips, based on the excitement of each of the video clips.

Method and apparatus for selecting video clip, server and medium

Embodiments of the present disclosure provide a method and apparatus for generating an image. The method may include: receiving a first image including a face input by a user in an interactive scene; presenting the first image to the user; inputting the first image into a pre-trained generative adversarial network in a backend to obtain a second image output by the generative adversarial network; where the generative adversarial network uses face attribute information generated based on the input image as a constraint; and presenting the second image to the user in response to obtaining the second image output by the generative adversarial network in the backend.

Method and apparatus for generating face image

The present application discloses a video-based human behavior recognition method, apparatus, device and storage medium, and relates to the technical field of human recognitions. The specific implementation scheme lies in: acquiring a human rectangle of each video frame of the video to be recognized, where each human rectangle includes a plurality of human key points, and each of the human key points has a key point feature; constructing a feature matrix according to the human rectangle of the each video frame; convolving the feature matrix with respect to a video frame quantity dimension to obtain a first convolution result and convolving the feature matrix with respect to a key point quantity dimension to obtain a second convolution result; inputting the first convolution result and the second convolution result into a preset classification model to obtain a human behavior category of the video to be recognized.

Video-based human behavior recognition method, apparatus, device and storage medium

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Beijing, China

Fu Li