Abstract: Provided is a method for retrieving a lost-of-contact mobile terminal. The method is applied to a first control terminal, and includes: receiving a first lost-of-contact distress signal a first mobile terminal forwarded by a server; displaying a help seeking interface; sending, in response to a help-retrieving instruction, a first help-retrieving request to the server; and receiving a first confirmation help message from a second control terminal from the server, wherein the first confirmation help message indicates that the second control terminal agrees to help retrieve the first mobile terminal.

Abstract: Provided is a method for processing video data. The method includes: acquiring a second outline of at least one object in an (n+1)th image frame of a to-be-compressed video data group based on a fuzzy algorithm; determining a motion vector of the at least one object according to the second outline and a first outline of the at least one object in an nth image frame of the to-be-compressed video data group; acquiring compressed video data according to the motion vector and a start image frame of to-be-compressed video data; and sending the compressed video data to an apparatus for displaying images.

Abstract: The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

Abstract: Provided are a mixed reality display method, a mixed reality device, and a storage medium. The method includes: after receiving a mixed display signal, tracking the eyeballs of a user by means of an eyeball tracker, and determining a gaze point of the user on a display screen; determining, according to a correspondence between the gaze point and a sub-display region in the display screen, the sub-display region the user gazes; determining at least one first image sensor according to a correspondence between the sub-display region and an image sensor in an image sensor array, and adjusting the at least one first image sensor; and superimposing and rendering an environment image output by the image sensor array and a virtual image, and obtaining and displaying an MR image.

Abstract: A control method for display switching, an electronic device, and a storage medium are provided. The control method for display switching includes: establishing a first buffer and a second buffer at a kernel layer and initializing the first buffer; establishing a first service process and a second service process at a user layer, and controlling the first service process to render a first image according to a display parameter of a first buffer and transmit it to a display screen via the first buffer for display; and initializing the second buffer according to a switching instruction, and controlling the second service process to render a second image according to a display parameter of the second buffer and transmit it to the display screen via the second buffer so as to make the display screen switch to displaying the second image.

Abstract: The embodiments of the present application disclose a method of controlling display of a display device, an apparatus thereof and a display apparatus. The display device includes a backlight unit and a display panel, wherein the backlight unit includes a plurality of backlight partitions, each of which is independently driven, and the display panel includes a plurality of display partitions in one-to-one correspondence with the backlight partitions. The method comprises: causing the display panel to display a test image, wherein each pixel of the test image has a same gray value; acquiring a luminance of each display partition of the display panel; determining a compensation coefficient according to the luminance of each display partition; adjusting backlight data of the backlight partition according to the compensation coefficient to obtain adjusted backlight data of each backlight partition; and providing the adjusted backlight data to the backlight unit for display by the display panel.

Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the

Abstract: The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m1 . . . mn . . . mN} and corresponding center frequencies {?1 . . . ?n . . . ?N}; (4) searching a fault related modal mI, guiding parameter optimization by using a center frequency ?I of the fault related modal mI, and retrieving an optimal target component mI including fault information; and (5) performing envelopment analysis on the optimal target component mI, and diagnosing the rotary machine equipment according to an envelope spectrum.

Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.

Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.

Abstract: This disclosure relates to an image segmentation method and apparatus and image three-dimensional reconstruction method and apparatus, and to the field of computer technology. The segmentation method includes: dividing pixels in an image to be segmented among different pixel sets, according to color gamut ranges to which pixel values of the pixels belong; determining matching between pixels in each pixel set according to pixel values; and performing image segmentation on the image to be segmented according to the matching.

Abstract: The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

Abstract: The embodiments of the present application disclose a method of controlling display of a display device, an apparatus thereof and a display apparatus. The display device includes a backlight unit and a display panel, wherein the backlight unit includes a plurality of backlight partitions, each of which is independently driven, and the display panel includes a plurality of display partitions in one-to-one correspondence with the backlight partitions. The method comprises: causing the display panel to display a test image, wherein each pixel of the test image has a same gray value; acquiring a luminance of each display partition of the display panel; determining a compensation coefficient according to the luminance of each display partition; adjusting backlight data of the backlight partition according to the compensation coefficient to obtain adjusted backlight data of each backlight partition; and providing the adjusted backlight data to the backlight unit for display by the display panel.

Abstract: A control method for display switching, an electronic device, and a storage medium are provided. The control method for display switching includes: establishing a first buffer and a second buffer at a kernel layer and initializing the first buffer; establishing a first service process and a second service process at a user layer, and controlling the first service process to render a first image according to a display parameter of a first buffer and transmit it to a display screen via the first buffer for display; and initializing the second buffer according to a switching instruction, and controlling the second service process to render a second image according to a display parameter of the second buffer and transmit it to the display screen via the second buffer so as to make the display screen switch to displaying the second image.

Abstract: The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m1 . . . mn . . . mN} and corresponding center frequencies {?1 . . . ?n . . . ?N}; (4) searching a fault related modal mI, guiding parameter optimization by using a center frequency ?I of the fault related modal mI, and retrieving an optimal target component mI including fault information; and (5) performing envelopment analysis on the optimal target component mI, and diagnosing the rotary machine equipment according to an envelope spectrum.

Abstract: A layout method of a chip includes: determining a logic diagram corresponding to a chip to be laid out and a device list corresponding to the logic diagram; and determining a layout diagram of the chip to be laid out, according to the logic diagram, the device list, and a pre-trained layout mode. The layout diagram includes at least an arrangement position, in the chip to be laid out, of each device in the device list.

Abstract: The present invention discloses a method for predicting bearing life based on a hidden Markov model (HMM) and transfer learning, including the following steps: (1) acquiring an original signal of full life of a rolling bearing; and extracting a feature set including a time domain feature, a time-frequency domain feature, and a trigonometric function feature; (2) inputting the feature set into an HMM to predict a hidden state, to obtain a failure occurrence time (FOT); (3) constructing a multilayer perceptron (MLP) model, obtaining a domain invariant feature and an optimal model parameter, and obtaining a neural network life prediction model; and (4) inputting the remaining target domain feature sets into the neural network life prediction model, and predicting the remaining life of the bearing. In the present invention, MLP-based transfer learning is used to resolve distribution differences in a source domain and a target domain caused by different operating conditions.