48 results about "Scale down" patented technology

The invention discloses a deep learning point cloud registration method for low-overlapping-rate point clouds. The method comprises the following steps: (1) carrying out multi-scale down-sampling on an input point cloud, calculating a weighted geometric centroid and a normal vector based on an adaptive neighborhood, and constructing a stable local reference point; (2) proposing a centroid geometric structure code, and embedding an attention mechanism by taking an inter-centroid Euclidean distance, a normal included angle and a neighborhood radius ratio as geometric priori to realize feature matching of structure constraint; (3) in a training stage, optimizing overlapping region positioning and point pair matching consistency by adopting a joint loss function formed by circular ring feature loss, overlapping perception loss and matchability loss; and (4) rigid body transformation is estimated by combining a coarse-to-fine matching strategy with a random sampling consistency algorithm, and global high-precision alignment is realized. According to the method, the registration precision and robustness under the conditions of low overlapping rate, noise and shielding are effectively improved, and the method is superior to an existing method in the aspects of a standard data set and real measurement data.

A device may be configured to receive a bitstream. The device may decode the bitstream according to a video coding technique. The device may determine a normalization technique from one or more normalization techniques. The device may perform the normalization technique on the decoded bitstream to generate feature data for input into a neural network. Normalizations techniques may include a combination of a linear normalization techniques, scaling down normalization techniques, and logarithmic normalization techniques.

The present embodiments relate to a write head with an asymmetrical side gap (SG) design. In the asymmetrical design, the main pole can be disposed offset to a central axis of the write head such that the main pole is closer to a first side shield (SS) portion than a second SS portion. The asymmetrical design can be achieved using an ion-beam etching (IBE) or ion-beam deposition (IBD) process. The asymmetrical design can provide a narrower side gap width while mitigating any writability limitations or constraints caused by scaling down the SG.

1. Name of the product in this design: Experimental Demonstration Platform (VMO Model Scaled Down). 2. Purpose of this design: A platform for aircraft experimental demonstrations. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention provides a method for determining the size of a pulse wind tunnel test model, comprising: determining the flow field of the nozzle and test section using numerical simulation to obtain a uniform flow field region S1 in the wind tunnel; then calibrating the uniform flow field region S2 of the nozzle and test section using a cross-shaped frame, comparing the sizes of the two uniform flow fields S1 and S2, and selecting the smaller uniform flow field region S0 as the uniform flow field region S0; scaling down the selected model under the condition of maximum test angle, with the maximum projected area M1 of the model in the direction perpendicular to the airflow occupying 60% to 65% of the cross-sectional area of ​​the uniform flow field S0; integrating the scaled-down model with the nozzle and test section into a single model, numerically simulating the entire flow process under the condition of maximum angle of attack and / or sideslip angle test angle, redetermining the model scale based on the model installation position, the shock wave position at the leading edge of the model, and the coverage of the uniform flow field, and determining the upper limit of the model length through the effective test time and the velocity of the flow field in the test section.

A crystallizer modeling method according to an embodiment of the present disclosure may comprise the steps of: calculating a first stress distribution of a first crystallizer; setting a parameter required for modeling a second crystallizer scaled down from the first crystallizer; calculating a second stress distribution of the second crystallizer on the basis of the set parameter; comparing the second stress distribution with the first stress distribution; determining a parameter for calculating a second stress distribution matching the first stress distribution; and designing the second crystallizer on the basis of the parameter.

This invention relates to the fields of computer network technology and game server technology, specifically to a method and system for automatic scaling up and down and load balancing of distributed game servers. The method involves: configuring operational indicators and corresponding thresholds to trigger scaling up and down; collecting real-time data and comparing it with the thresholds; and determining whether to trigger a scaling up or down process based on the comparison result. If a scaling up process is triggered, server resources are automatically used to deploy new stateful service nodes, synchronize basic data and service registration, and allocate new users to the new stateful service nodes using a load balancing algorithm. If a scaling down process is triggered, stateful service nodes of the combat suit type are scaled down. This invention can achieve fully automated scaling up and down and precise load balancing, improving service response efficiency and optimizing resource utilization.

The invention provides an information processing method, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: monitoring the number of producers, and enabling the producers to be deployed in a container arrangement class framework; when it is determined that the number of producers changes, it is determined that capacity expansion or capacity reduction needs to be conducted on consumers, and the consumers are deployed in the container arrangement class framework; according to the number of the producers and first information, capacity expansion or capacity reduction is carried out on the consumers, and the first information represents the proportional relation between the number of the producers and the number of the consumers. According to the scheme provided by the invention, the universality is good, the business fluctuation can be responded more quickly, and the capacity of consumers can be expanded and shrunk in time.

The invention provides a high-precision sub-nanometer displacement table. The high-precision sub-nanometer displacement table comprises a guide switching hinge, a displacement zooming driving assembly and a switching frame assembly. The displacement scaling driving assembly comprises a displacement scaling driving hinge and a piezoelectric actuator, the displacement scaling driving hinge is of an integrated sheet structure, the piezoelectric actuator is in contact with the input end of the displacement scaling driving hinge in a magnetic pre-tightening mode, an eccentric hinge structure is arranged in the displacement scaling driving hinge, and the piezoelectric actuator is in contact with the input end of the displacement scaling driving hinge. The displacement scaling driving assembly is used for scaling down the input displacement of the piezoelectric actuator according to the proportion and then outputting the input displacement through the displacement output block, the guide transfer hinge is in hard connection with the displacement output block of the displacement scaling driving assembly, and the transfer frame assembly is used for fixedly installing the guide transfer hinge and the displacement scaling driving assembly. According to the invention, precise positioning requirements under harsh working conditions such as semiconductor manufacturing and synchrotron radiation beam line regulation and control can be met.

The invention discloses a high-resolution unmanned aerial vehicle multi-scale remote sensing image target detection method and device, and belongs to the technical field of target detection, and the method comprises the steps: introducing a target activation network into a backbone network to generate an object activation graph, cutting out a region block containing a target from a feature level through uniform cutting and mapping, and reducing the calculation; a space-channel collaborative attention module and an enhanced multi-level feature reconstruction module are introduced into the neck network to process each layer of feature map and the activation enhancement feature map output by the backbone network to obtain and output high-scale fusion features of each level and low-scale down-sampling features of each level; a dynamic mask sparse calculation module is introduced into the detection network to generate a hard mask of a key area in neck network output features, sparse convolution of the neck network output features is guided by the hard mask to obtain sparse features, classification and bounding box regression are executed after multi-scale sparse feature fusion is performed on all levels of sparse features, and a target detection result is output. The detection speed and the detection accuracy can be improved.

The present application relates to the technical field of computers. Disclosed are an image scaling method and apparatus. The method comprises: receiving an instruction for instructing the scaling-down and scaling-up of an image frame, and calculating the ratio of the height of the adjusted image frame indicated in the instruction to the height of a display screen of a target vehicle; and on the basis of the ratio, scaling down or scaling up an original image frame, which comprises information required to be displayed in a dialog service flow interface, so as to obtain a target image frame, which comprises the information required to be displayed in the dialog service flow interface, and displaying the target image frame in the dialog service flow interface.

A device may be configured to receive a bitstream. The device may decode the bitstream according to a video coding technique. The device may reconstruct feature data from the decoded bitstream. The device may determine a normalization technique from one or more normalization techniques. The device may perform the normalization technique on reconstructed feature data to generate feature data for input into a neural network. Normalizations techniques may include a combination of a linear normalization techniques, scaling down normalization techniques, and logarithmic normalization techniques. In one example, a normalization technique may include a transform based normalization technique. In one example, parameters of a transform based normalization technique may be set based on a picture resolution.

The utility model discloses a kind of simulation car models that are scaled down, car body and the two walking mechanisms of car body bottom end;Each the walking mechanism includes two wheel hubs, two the wheel hub is jointly connected with connecting mechanism between, and two wheel hub outer edge surface is all sleeved with tire Installation;The connecting mechanism includes long shaft, the long shaft both ends are all equidistant with several limit plates, and long shaft both ends are all rotationally installed with locking mechanism, each the limit plate outside is all provided with limit post, the limit post is away from the one end of limit plate and is provided with limit block, the wheel hub inner edge surface is provided with four limit slots, four the limit slot is symmetrically distributed on wheel hub inner edge surface upside and downside, the utility model has the advantages of facilitating dismounting wheel, solve the current simulation car's wheel dismounting more difficult, affect the production and maintenance efficiency of simulation car Problem.

A device may be configured to receive a bitstream. The device may decode the bitstream according to a video coding technique. The device may reconstruct feature data from the decoded bitstream. The device may determine a normalization technique from one or more normalization techniques. The device may perform the normalization technique on reconstructed feature data to generate feature data for input into a neural network. Normalizations techniques may include a combination of a linear normalization techniques, scaling down normalization techniques, and logarithmic normalization techniques. In one example, a normalization technique may include a transform based normalization technique.

Embodiments of this application disclose a video image processing method and apparatus. A specific solution is as follows: obtaining identity information and location information of each subject in an ith video image frame; determining M main subjects from the ith video image frame based on identity information of subjects in N video image frames before the ith video image frame, where the identity information of the subjects in the N video image frames includes identity information of the M main subjects; cropping the ith video image frame based on location information of the main subjects, where a cropped ith video image frame includes the M main subjects; and scaling down or scaling up the cropped ith video image frame, so that a display displays the cropped ith video image frame based on a preset display specification.

This application discloses an experimental method based on a nozzle-simulated helicopter water spraying fire extinguishing test platform, including the following steps: Step S1: Select the nozzle model according to the morphology of the pouring water curtain formed during actual helicopter bucket fire extinguishing; Step S2: Measure the droplet size formed by the actual helicopter bucket water spraying; Step S3: Use the grid paper method to determine the nozzle that sprays water at a preset spray angle through the nozzle-simulated helicopter water spraying fire extinguishing test platform; Step S4: Based on computational fluid dynamics, establish a three-dimensional model of the helicopter bucket and a numerical computational fluid domain, perform mesh generation, and use the VOF-DPM multiphase flow model to simulate the bucket water spraying process; Step S5: According to the Froude similarity criterion, calculate the water spraying distribution area obtained in Step S4. S p The water spray volume L of the experimental sprinkler head was obtained by geometric scaling down using a scaling factor n. n This method provides a controllable and repeatable experimental environment for studying helicopter bucket water-dropping firefighting.

The invention discloses a power grid self-healing logic verification method and system, and the method comprises the steps: reducing key parameters in an actual power grid according to a dynamic proportion based on a target voltage level, adjusting an adjustable hardware unit, and connecting nodes in the adjustable hardware unit, thereby obtaining a miniature power grid; and if the difference between the actual parameter of the miniature power grid and the corresponding target parameter does not meet the preset condition, readjusting the adjustable hardware unit, loading the to-be-verified self-healing logic, selecting a fault type, carrying out fault simulation, and executing an action based on the to-be-verified self-healing logic to realize self-healing of the miniature power grid. And determining self-healing verification indexes according to the self-healing process data of the miniature power grid, judging the effectiveness of the self-healing logic to be verified, and replacing the target voltage grades, thereby obtaining the self-healing verification indexes corresponding to the target voltage grades, and analyzing the adaptability of the self-healing logic to be verified. Therefore, the verification efficiency can be improved, the risk is low, the problem of single voltage adaptation can be solved, and the simulation precision can be improved.

A cellular communication network includes one or more computing devices and configured to process connections to user equipment, each computing device of the one or more computing devices executing one or more software components. Each computing device of the one or more computing devices is configured to: receive a plurality of signals from the one or more microservices; process the plurality of signals with a machine learning model to obtain a predicted loading by the user equipment; and scale up or scale down at least one of the microservices according to the predicted loading.

The embodiment of the invention relates to the technical field of reactor testing, in particular to a method for determining the reduction proportion of a simulated reactor core of a simulated reactor relative to an actual reactor core of an actual reactor, the simulated reactor, a design verification and safety checking method of the reactor and a computer storage medium. According to the method for determining the reduction ratio of the simulated reactor core of the simulated reactor relative to the actual reactor core of the actual reactor, the resistance characteristic of the simulated reactor core of the simulated reactor is similar to that of the actual reactor core of the actual reactor; the rod bundle heat exchange characteristic and the flow distribution characteristic of the fuel assembly simulation piece of the simulation reactor core are similar to those of an actual reactor core of an actual reactor, so that the simulation reactor can meet the similar requirements of a natural circulation experiment; moreover, by determining the proportion of the number of the fuel assemblies of the actual reactor reduced to the number of the fuel assemblies of the simulated reactor, the number of the fuel assembly simulators of the simulated reactor can be greatly reduced under the condition of ensuring the simulation precision, and the experiment cost of the natural circulation experiment is reduced.

A device may be configured to receive a bitstream. The device may decode the bitstream according to a video coding technique. The device may reconstruct feature data from the decoded bitstream. The device may determine a normalization technique from one or more normalization techniques. The device may perform the normalization technique on reconstructed feature data to generate feature data for input into a neural network. Normalizations techniques may include a combination of a linear normalization techniques, scaling down normalization techniques, and logarithmic normalization techniques. In one example, a normalization technique may include a transform based normalization technique.

The application relates to the technical field of terminals, in particular to an image recognition method, an electronic device, a storage medium, a program product and a chip. In the method, the electronic device can acquire a first image to be processed, and perform multi-scale down-sampling on the first image to obtain a plurality of down-sampled images. Then, the electronic device can determine a plurality of target image blocks from the plurality of down-sampled images, and recognize image information in the first image according to the plurality of target image blocks. The target image blocks include valid information of the first image. In this way, the plurality of down-sampled images are obtained by performing multi-scale down-sampling on the first image, the target image blocks containing valid information are determined from the plurality of down-sampled images, the image information in the first image is recognized, the image blocks containing invalid information do not need to be subjected to image recognition, and the computing resources of the electronic device can be reduced.

An image processing method includes the steps of: receiving an image signal, wherein the image signal comprises a frame; performing a motion estimation operation on a plurality of blocks within the frame to generate a plurality of first motion vectors, respectively; scaling down the frame to generate a scaled-down frame; performing the motion estimation operation on a specific block within the scaled-down frame to generate a second motion vector corresponding to the specific block; and determining a plurality of final motion vectors of the plurality of blocks of the frame according to the plurality of first motion vectors and the second motion vector.

A method includes receiving, at an Open Radio Access Network (Open RAN) RAN Intelligent Controller (RIC) 104, a plurality of RIC indication messages from a plurality of gNodeBs (gNBs) 100. Each of the plurality of RIC indication messages comprises one or more network parameters. The method further includes determining whether a plurality network conditions satisfy one or more predefined criteria based on the one or more network parameters. The plurality network conditions comprise an identification of a scale-in, an expiration of a session drain timer, and an absence of any scale-out. The method further includes transmitting a RIC control request in response to the plurality network conditions that satisfy one or more predefined criteria. The RIC control request is transmitted to initiate a change of Centralized Unit User Plane (CU-UP) procedure for a set of User Equipments (UEs) associated with at least one gNB among the plurality of gNBs 100.

An embodiment for dynamic optimization of power consumption in storage systems is provided. The embodiment may include receiving historical time-series data from one or more components in a data center. The embodiment may also include predicting one or more workload metrics of the one or more components during a pre-defined time range. The embodiment may further include identifying one or more required resources for the one or more components to handle the predicted one or more workload metrics during the pre-defined time range. The embodiment may also include in response to determining at least one component of the one or more components does not require a resource allocation increase during the pre-defined time range, executing a first action to scale down at least one first resource during the pre-defined time range.

According to implementations of the present disclosure, a scheme for image scaling is proposed. According to the scheme, an input image having a first resolution is obtained. An output image having a second resolution and high-frequency information subject to a predetermined distribution are generated based on the input image by using a trained invertible neural network, where the first resolution is higher than the second resolution, and the input image and the output image have the same semantics. Furthermore, another input image having the second resolution is obtained. Another output image having the first resolution is generated based on the other input image and the high-frequency information subject to the predetermined distribution by using an inverse network of the invertible neural network, where the other input image and the other output image have the same semantics. The scheme is capable of scaling down original images into low-resolution images with the same semantics and visually pleasing, and is capable of reconstructing high-quality high-resolution images from the low-resolution images.

The invention discloses a high-temporal-spatial-resolution atmospheric particulate matter source analysis method based on observation constraint. The method comprises the following steps: simulating set time, and setting regional particulate matter PM2.5 and concentration of each component thereof; assimilating the observed PM2.5 and component concentration data of the PM2.5 in the set time and the set region and the PM2.5 and component concentration data of the PM2.5 obtained through CMAQ simulation; performing source analysis based on the assimilated data; and constructing a machine learning model by taking the source contribution of the source analysis as a dependent variable and taking the meteorological data, the geographic and social economic data and the road traffic data in each grid of the set time and the set region as independent variables, and simulating the source contribution concentration of different sources through the constructed model in a scale reduction manner. By adopting the method provided by the invention, an accurate source analysis result with high temporal-spatial resolution can be obtained, so that a scientific basis is provided for accurately formulating an atmospheric pollution prevention and control policy, a treatment strategy is optimized, the treatment cost is reduced, and the treatment efficiency is improved.

The invention provides a monocular space target attitude estimation method and device and electronic equipment, and belongs to the technical field of artificial intelligence, and the method comprises the steps: inputting a monocular image of a space target into a feature enhancement model, and obtaining a multi-scale feature map; performing down-sampling on the multi-scale feature map by a frequency domain-space domain combined down-sampling layer based on wavelet transform and a channel attention mechanism, and outputting a multi-scale down-sampling feature map; performing down-sampling on a small-scale down-sampling feature map in the multi-scale down-sampling feature map based on the feature map of the associated image by a feature collaborative enhancement down-sampling layer, and outputting a cross-image down-sampling feature map; performing feature fusion and feature enhancement on the multi-scale down-sampling feature map and the cross-image down-sampling feature map by a feature enhancement pyramid layer, and outputting an enhanced multi-scale feature map; and estimating the attitude of the space target based on the enhanced multi-scale feature map to obtain an attitude estimation result of the space target. According to the invention, the accuracy of attitude estimation can be improved.

The present application relates to new energy automobile energy consumption management and artificial intelligence prediction technical field, specifically relates to a kind of energy consumption prediction method based on multi-source physical characteristics and multi-scale recursive convolution, comprising: step S1: multi-source physical characteristics acquisition and joint embedding;Step S2: multi-scale down-sampling and recursive trend injection;Step S3: local slice convolution and heterogeneous feature fusion;Step S4: double-path deep feature fitting and residual compensation;Step S5: result reconstruction and energy consumption prediction output.The present application has the technical effect that by constructing multidimensional collaborative time feature extraction system, the precision and robustness of energy efficiency prediction under complex physical working conditions are significantly improved.

The invention discloses a three-dimensional geologic structure modeling method based on attention enhancement SinGAN, and relates to the technical field of geologic modeling, and the three-dimensional geologic structure modeling method based on attention enhancement SinGAN mainly comprises the steps: carrying out the multi-scale down-sampling of a single training image and condition drilling data, and obtaining a multi-scale data pyramid; and training the constructed attention-enhanced conditional single image generative adversarial network of multiple stages, and performing conditional geological simulation on new conditional drilling data by using the trained attention-enhanced conditional single image generative adversarial network to obtain a high-resolution three-dimensional geological structure model. By implementing the three-dimensional geological structure modeling method based on the attention enhancement SinGAN provided by the invention, the precision and efficiency of multi-scale and conditional constraint geological simulation can be improved.

The invention relates to the technical field of computer vision, and discloses a multi-weather image restoration method and device based on coding and decoding double-prompt learning. The method comprises the following steps: performing multi-scale down-sampling to generate a multi-scale degraded image; carrying out feature extraction to obtain image features, adaptively generating coding prompt information by adopting a multi-scale coding prompt method, carrying out weighted fusion on the image features and the coding prompt information to obtain fusion features, and carrying out image feature extraction on the fusion features to obtain deepest image features; according to the deepest image features, adaptively generating decoding prompt information, carrying out double-attention fusion on the decoding prompt information and the image features, carrying out splicing fusion on the decoding prompt information and the image features to obtain fusion information, carrying out feature reconstruction according to the fusion information, and carrying out image reconstruction on a reconstructed feature map to obtain a restored image. According to the method, the adaptive capacity to various degradation types and the processing capacity to complex degradation phenomena can be improved.