Abstract: An advanced monitoring device and an implementation method for a whole-process deformation curve of a surrounding rock during tunnel excavation is disclosed, comprising a steel pipe elastic body, a cathetometer structure and an embedded optical fiber, and an implementation step; the cathetometer is an equidistant series structure, and fixed in the steel pipe; the embedded optical fiber is encapsulated in the surface slot of the steel pipe; the cathetometer and the embedded optical fiber and the steel pipe form a deformation coordination structure, and the deformation of the surrounding rock can be deduced by calculating the variation of the cathetometer and the deformation of the optical fiber. The invention can test and calculate the deformation curve of the surrounding rock in front of the excavation face during tunnel excavation, and provide support for engineering dynamic design, construction and safety.

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 power inverter with liquid cooled busbars includes multiple AC power outlets. The power inverter also includes a busbar having a busbar arm connected to one of the AC power outlets, and a busbar leg having a first end connected to the busbar arm. The busbar leg is at least partially situated in a cooling channel of the power inverter, which may be a built-in cooling channel or a detachable cooling channel. A second end of the busbar leg extends beyond the cooling channel and is exposed for electrical connection.

Abstract: The present disclosure relates to an image re-ranking method, which includes: performing image searching by using an initial keyword, obtaining, by calculation, an anchor concept set of a search result according to the search result corresponding to the initial keyword, obtaining, by calculation, a weight of a correlation between anchor concepts in the anchor concept set, and forming an anchor concept graph ACG by using the anchor concepts in the anchor concept set as vertexes and the weight of the correlation between anchor concepts as a weight of a side between the vertexes; acquiring a positive training sample by using the anchor concepts, and training a classifier by using the positive training sample; obtaining a concept projection vector by using the ACG and the classifier; calculating an ACG distance between images in the search result corresponding to the initial keyword; and ranking the images according to the ACG distance.

Abstract: Present invention provides a ranking convolutional neural network constructing method and an image processing method and apparatus thereof. The ranking convolutional neural network includes a ranking layer that is configured to sort an output of a previous layer of the ranking layer, generate an output of the ranking layer according to the sorted output, and output the output of the ranking layer to a next layer of the ranking layer. Using the ranking convolutional neural network enables obtaining an output feature corresponding to the input feature image through automatic learning. Compared with prior art methods that obtain features through manual calculation, the method of the present invention is superior in terms of reflecting the objective laws contained by the patterns of the actual scene. When applied to the field of image processing, the method can significantly improve the effect of image processing.

Abstract: The present application provides a method and an apparatus for parsing and processing a three-dimensional CAD model, where the method includes: determining three kinds of adjacency relation information for each component in the three-dimensional model; performing aggregation processing on all components of the three-dimensional model, and generating three part hypothesis sets for the three-dimensional model; performing voxelization expression processing on each part hypothesis in each part hypothesis set, and generating voxelization information for each part hypothesis; inputting voxelization information of all part hypotheses in each part hypothesis set into an identification model to obtain a confidence score and a semantic category probability distribution for each part hypothesis; and constructing, according to the confidence score and the semantic category probability distribution of each part hypothesis in the part hypothesis sets, a high-order conditional random field model, and obtaining a semantic ca

Abstract: The present application relates to post estimation methods and apparatuses, and computer systems, wherein the method includes: extracting a feature in each body part of body parts of an object in an image to generate feature maps for the each body part, wherein the each body part corresponds to at least one part type; predicting part type scores respectively for features in each of the feature maps and establishing part type score maps based on the predicted part type scores; optimizing, for at least one point in each of the part type score maps, the established part type score maps based on a message related to an adjacent body part of a body part corresponding to the point; and determining an estimated position and an estimated part type of the each body part based on the optimized part type score maps to obtain an estimated pose of the object.

Abstract: Image detection methods and apparatuses, electronic devices and storage media include: down-sampling a second image to obtain a first image; determining at least one key sub-region in the second image for category detection; respectively extracting a feature of the first image and a feature of the at least one key sub-region; and generating a category detection result of the second image at least based on the extracted features.

Abstract: A data storage system executes a system management component initiating and monitoring a multi-task job affecting a storage resource such as a storage device. Execution includes maintaining a job data object and a set of task data objects for the multi-task job. A task operates upon the storage resource has a respective task data object containing an identifier of the storage resource as a task-affected storage resource. The job data object is used by a job-level function (e.g., job level of a management GUI) and initially lacks the identifier of the storage resource. During execution, the identifier of the storage resource is copied from the task data object to the job data object as an identifier of a job-affected storage resource, which is then used in subsequent performance of the job-level function (e.g., displaying a hyperlink for the storage resource in a job-level screen of the management GUI).

Abstract: A vegetable oil-based cartilage bionic cushioning and shock-absorbing material, and a preparation method and use thereof, is provided. The vegetable oil-based cartilage bionic cushioning and shock-absorbing material is prepared from a premix A and an isocyanate mixture B; the premix A including a vegetable oil-based modified polyol, a type 1 polyether polyol, a type 2 polyether polyol, a polymer polyol, a surfactant, a foaming agent, a chain extender, a catalyst and a cell regulator; the type 1 polyether polyol is a polyether polyol with a molecular weight of 400-1000 and a hydroxyl value of 110-280 mgKOH/g; and the type 2 polyether polyol is a polyether polyol with a molecular weight of 1000-10000 and a hydroxyl value of 25-56 mg KOH/g. The material provided by the present invention is environment-friendly and breathable with open cells, and has a high cushioning effect and a low permanent compression set value.

Abstract: Systems and methods for training a residual neural network are described. One of the methods includes: initializing one or more weights of each of a plurality of residual units; inputting a plurality of training cases to a first warp unit in a series; using each training case to optimize the one or more weights for each residual unit in parallel in the first warp unit in the series; starting with the output of the first warp unit in the series, iteratively propagating the output of each warp unit to the input of a next respective warp unit in the series, for each respective warp unit, using each training case to optimize the one or more weights for each residual unit in parallel in the respective warp unit; and storing the output of the last warp unit in the series and the weights for each residual unit.

Abstract: A method for preparing a metal composite plate by rolling includes the following steps: 1) rolling composite surfaces of a base plate and a cladding plate, respectively, to obtain the base plate corrugation and the cladding plate corrugation for mating with each other; 2) cleaning the composite surfaces of the base plate and the cladding plate to expose the metal matrixes of the base plate and the cladding plate; 3) laminating the base plate and the cladding plate sequentially so that the base plate corrugation on the base plate and cladding plate corrugation on the cladding plate mate with each other, compacting, and performing welding sealing treatment to the base plate and the cladding plate to obtain a composite plate slab; and 4) rolling the composite plate slab after inspection by using a compositing machine to a desirable thickness, to obtain a metal composite plate.

Abstract: An object detection method, a neural network training method, an apparatus, and an electronic device include: obtaining, through prediction, multiple fused feature graphs from images to be processed, through a deep convolution neural network for target region frame detection, obtaining multiple first feature graphs from a first subnet having at least one lower sampling layer, obtaining multiple second feature graphs from a second subnet having at least one upper sampling layer, and obtaining fused graph by fusing multiple first feature graphs and multiple second feature graphs respectively; and obtaining target region frame data according to the multiple fused feature graphs. Because the fused feature graphs better represent semantic features on high levels and detail features on low levels in images, target region frame data of big and small objects in images can be effectively extracted according to the fused feature graphs, thereby improving accuracy and robustness of object detection.

Abstract: A visual semantic complex network system and a method for generating the system have been disclosed. The system may comprise a collection device configured to retrieve a plurality of images and a plurality of texts associated with the images in accordance with given query keywords; a semantic concept determination device configured to determine semantic concepts of the retrieved images and retrieved texts for the retrieved images, respectively; a descriptor generation device configured to, from the retrieved images and texts, generate text descriptors and visual descriptors for the determined semantic concepts; and a semantic correlation device configured to determine semantic correlations and visual correlations from the generated text and visual descriptor, respectively, and to combine the determined semantic correlations and the determined visual correlations to generate the visual semantic complex network system.

Abstract: Disclosed is an apparatus for face verification. The apparatus may comprise a feature extraction unit and a verification unit. In one embodiment, the feature extraction unit comprises a plurality of convolutional feature extraction systems trained with different face training set, wherein each of systems comprises: a plurality of cascaded convolutional, pooling, locally-connected, and fully-connected feature extraction units configured to extract facial features for face verification from face regions of face images; wherein an output unit of the unit cascade, which could be a fully-connected unit in one embodiment of the present application, is connected to at least one of previous convolutional, pooling, locally-connected, or fully-connected units, and is configured to extract facial features (referred to as deep identification-verification features or DeepID2) for face verification from the facial features in the connected units.

Abstract: A method and a system for pose estimation are provided. The method includes: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

Abstract: The disclosures relate to a method and a system for pose estimation. The method comprises: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

Abstract: The present application provides a method and an apparatus for parsing and processing a three-dimensional CAD model, where the method includes: determining three kinds of adjacency relation information for each component in the three-dimensional model; performing aggregation processing on all components of the three-dimensional model, and generating three part hypothesis sets for the three-dimensional model; performing voxelization expression processing on each part hypothesis in each part hypothesis set, and generating voxelization information for each part hypothesis; inputting voxelization information of all part hypotheses in each part hypothesis set into an identification model to obtain a confidence score and a semantic category probability distribution for each part hypothesis; and constructing, according to the confidence score and the semantic category probability distribution of each part hypothesis in the part hypothesis sets, a high-order conditional random field model, and obtaining a semantic ca

Abstract: The present invention provides a method for video matting via sparse and low-rank representation, which firstly selects frames which represent video characteristics in input video as keyframes, then trains a dictionary according to known pixels in the keyframes, next obtains a reconstruction coefficient satisfying the restriction of low-rank, sparse and non-negative according to the dictionary, and sets the non-local relationship matrix between each pixel in the input video according to the reconstruction coefficient, meanwhile sets the Laplace matrix between multiple frames, obtains a video alpha matte of the input video, according to ? values of the known pixels of the input video and ? values of sample points in the dictionary, the non-local relationship matrix and the Laplace matrix; and finally extracts a foreground object in the input video according to the video alpha matte, therefore improving quality of the extracted foreground object.

Abstract: A method for predicting walking behaviors includes: encoding walking behavior information of at least one target object in a target scene within a historical time period M to obtain a first offset matrix for representing the walking behavior information of the at least one target object within the historical time period M; inputting the first offset matrix into a neural network, and outputting by the neural network a second offset matrix for representing walking behavior information of the at least one target object within a future time period M?; and decoding the second offset matrix to obtain the walking behavior prediction information of the at least one target object within the future time period M?.