119 results about "Scale down" patented technology

Various embodiments of the present disclosure are directed towards an image sensor comprising a pixel with a dual-PD layout for enhanced scaling down. The pixel spans a first integrated circuit (IC) die and a second IC die stacked with the first IC die. The pixel comprises a plurality of photodetectors in the first IC die, and further comprises a plurality of pixel transistors split amongst the first IC die and the second IC die. The plurality of photodetectors are grouped into one or more pairs, each having the dual-PD layout. A DTI structure completely and individually surrounds the plurality of photodetectors, and further extends completely through a substrate within which the plurality of photodetectors are arranged. As such, the DTI structure completely separates the plurality of photodetectors from each other.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a remote sensing image target detection method based on a multi-scale cross-stage network model, and the method comprises the following steps: obtaining a to-be-detected remote sensing image; the method comprises the following steps of: inputting a backbone network after passing through an input layer, extracting multi-scale and multi-level semantic features step by step, and obtaining feature maps of different sizes through multi-scale down-sampling; inputting the feature maps of different sizes into a feature integration network, and performing cross-stage fusion and lightweight optimization to obtain a plurality of fused feature maps; and inputting the plurality of fusion feature maps into a prediction head, and carrying out classification detection to obtain detection results of multiple scales. According to the method, the brand new multi-scale cross-stage network model is constructed, multi-scale feature extraction, fusion and target recognition capabilities are enhanced, accurate detection of multi-scale targets, especially small targets and complex-form targets, in the remote sensing image can be realized, detection precision and reasoning efficiency are both considered, and the method is suitable for remote sensing application scenes such as ocean monitoring and urban planning.

The invention provides a daily rainfall product downscaling and fusion cooperative calculation method and system based on deep learning, and belongs to the technical field of data processing, and the method comprises the steps: generating a daily scale data set and a monthly scale data set based on remote sensing rainfall products from various sources, meteorological station data, and vegetation and topographic data; constructing a data space-time fusion and downscaling model, and respectively training and respectively predicting the daily scale data set and the monthly scale data set by using the data space-time fusion and downscaling model to obtain daily scale precipitation estimation and monthly scale precipitation estimation; the daily scale precipitation estimation is corrected using the monthly scale precipitation estimation on the monthly scale. According to the scheme, downscaling is carried out on the monthly scale and the daily scale respectively, vegetation information data is added into the monthly downscaling to calculate the accurate monthly precipitation, the daily downscaling is used for estimating the proportion of the daily precipitation in the whole monthly precipitation, the two are multiplied to obtain the final daily precipitation, and the accuracy of daily scale precipitation estimation is improved.

The invention discloses a bridge disease detection method based on a diffusion model and a bitter fish optimization algorithm, and relates to the technical field of bridge detection. The method comprises the following steps: fixing a visual angle and a distance at an easy-to-peel or crack position of a bridge, and collecting and aligning visible light and near-infrared images; carrying out multi-scale downsampling on the image, and carrying out wavelet denoising, brightness correction and texture smoothing; inputting the preprocessing result into an improved diffusion model, weighting the edge during forward diffusion, and reversely generating and applying texture and contour smoothing; the multi-source feature channel and the reconstructed image are combined and input into a deep segmentation network, shadow and stain are eliminated by using a local difference function, and global search is performed on a segmentation threshold, a noise coefficient and the like based on a disease detection rate, a false detection rate and the like by using a bitter fish algorithm; training and correcting the high-noise area again according to the optimal parameters; and uniformly marking disease areas. According to the method, the recognition recall rate of tiny spalling and irregular cracks in an extreme environment can be greatly improved, and the intelligent level and the practical effect of bridge disease detection are improved.

The invention discloses a time sequence self-supervised learning method and system for a rail transit engineering video image, and the method comprises the steps: collecting an urban rail transit engineering construction video, and carrying out the segmentation of the video, and generating segmentation masks, so as to construct a self-supervised pre-training data set; a pre-trained model is selected and initialized; carrying out dynamic frame rate sampling processing on the input video; carrying out random shielding processing on each video frame to generate a corresponding binary mask image; carrying out multi-scale down-sampling on the processed video, and inputting the processed video into a coding-decoding network to complete spatial-temporal feature coding and generation of a reconstructed video and a prediction mask; a loss function is formed by calculating reconstruction loss of a reconstructed video and a real video, dynamic frequency loss based on Fourier domain low-frequency and high-frequency feature consistency and segmentation loss of a prediction mask and a real mask. The problems of insufficient time sequence modeling adaptability and insufficient multi-scale target adaptability are solved.

The invention provides a degradable breast implant which is of a three-dimensional network structure and comprises a surface part and a filling part, the surface part is a first three-dimensional virtual boundary and is located on the periphery of the breast implant, the appearance of the first three-dimensional virtual boundary is of a preset shape, the size of the first three-dimensional virtual boundary is of a preset size, and the center position of the surface part is a base point; the surface part is filled with the filling part, the filling part is of a dot matrix structure formed by a plurality of lattice units, the fiber thickness of the lattice units is the line diameter, the dot matrix structure comprises a plurality of dot matrix layers which are connected and communicated with one another, the shape of the dot matrix layers is the same as a preset shape, and the size of the dot matrix layers is scaled down relative to the preset size. The line diameters of the same dot matrix layer are the same, and the line diameters of different dot matrix layers are gradually reduced from the base point to the surface part. The line diameters of the different dot matrix layers are designed to be gradually reduced in the direction from the base point to the surface part, so that the problem of non-uniform breast tissue regeneration and growth caused by no difference of the degradation speed of the breast implant is successfully solved.

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.

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 invention provides a flow measurement device and method for an upper plate of a reactor core, and the device comprises a measurement model which is provided with a first to-be-measured drain hole which is scaled down according to the first drain hole; the at least one detection mechanism comprises a detection end, and the detection end is suitable for extending into a first to-be-detected drain hole needing to be measured and moving along a preset route, so that the detection mechanism generates a plurality of dynamic pressure data of a plurality of preset point positions, located on the preset route, in the first to-be-detected drain hole; and the flow calculation module is suitable for calculating the measured flow data of the first drain hole to be measured according to the plurality of dynamic pressure data, and performing proportional conversion according to the measured flow data to obtain the actual flow data of the first drain hole corresponding to the first drain hole to be measured. According to the flow measurement device, accurate measurement flow data can be obtained through calculation according to the dynamic pressure data, and then accurate actual flow data of the first drain hole are obtained.

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.

An autoscaling method and apparatus in a Kubernetes cluster, which method and apparatus are applied to the technical field of computers. The method comprises: in historical monitoring data, using an interpolation method to impute missing data between adjacent data points to obtain preprocessed sequence data; performing a fast discrete Fourier transform on the preprocessed sequence data to obtain a spectrogram of original data; analyzing the spectrogram, and identifying candidate periods in the spectrogram; using a preset correlation coefficient calculation function to verify the candidate periods to obtain a dominant period; on the basis of the dominant period and operation data in a cluster, performing peak prediction; and when a predicted peak exceeds a preset autoscaling threshold, triggering a scale-up or scale-down operation corresponding to a system. By means of the present disclosure, the requirement for resources in a period of time in the future can be predicted, and scaling up or scaling down can be performed, so that a more active and accurate resource allocation policy is realized, and the stability of a service is ensured.

The vehicle body leveling mechanism of the heavy down-the-hole drill comprises a drill chassis and a track frame, installation frames are welded to the left side and the right side of the drill chassis respectively, leveling oil cylinder assemblies are connected to the installation frames in a rotating mode, the lower portions of the leveling oil cylinder assemblies are connected to the track frame in a rotating mode, and the number of the leveling oil cylinder assemblies is two. According to the large-scale down-the-hole drill, the gravity center position of the large-scale down-the-hole drill is adjusted by synchronously controlling the oil cylinders to stretch out and draw back through the leveling oil cylinders and the electromagnetic valve assemblies, leveling is achieved, and the large-scale down-the-hole drill is suitable for large-angle climbing operation by adjusting the gravity center position of the large-scale down-the-hole drill.

The invention discloses an image rain removal method and device, which are used for solving the technical problem of poor image rain removal effect caused by difficulty in accurately balancing rain line removal and background detail recovery in the existing rain removal technology. The method comprises the following steps: acquiring a rain image, performing multi-scale down-sampling on the rain image, and outputting a multi-scale rain image; inputting the image with rain and the multi-scale image with rain into a preset multi-scale dual-domain collaborative rain removal network, wherein the preset multi-scale dual-domain collaborative rain removal network comprises an encoder, a multi-scale space interaction feature fusion module and a decoder; encoding the rain image and the multi-scale rain image by using an encoder, and outputting a plurality of target rain encoding features; carrying out feature fusion on the multiple target rain coding features through a multi-scale space interaction feature fusion module, and outputting target fusion features; and inputting the image with rain, the multi-scale image with rain, the target fusion feature and the target coding feature with rain into a decoder for decoding to generate a target rain-removed image.

The invention relates to the technical field of image processing, and provides a target detection method based on multi-scale feature weighted fusion. The method comprises the following steps: inputting a remote sensing image, and extracting multi-scale features to obtain a multi-level feature map; calculating a first adaptive fusion weight, performing up-sampling alignment on the feature maps of adjacent levels, performing weighted fusion on the feature maps by using the weight, and outputting a first fused feature map; calculating a second adaptive fusion weight, performing multi-scale down-sampling on the first fusion feature map to obtain a down-sampling fusion feature map, performing weighted fusion on the down-sampling fusion feature map by using the second weight, and outputting a second fusion feature; and constructing a target detection network by using a tag allocation strategy of a normalized Wasserstein distance, receiving the second fusion feature by the target detection network to perform target detection, and outputting a detection result, so as to solve the technical problems of insufficient detection precision and non-uniform anchor frame allocation, and achieve the technical effect of improving small target detection precision and tag allocation robustness.

A container-based application management method and apparatus are disclosed. A container-based serverless computing system is configured such that an application instance is allowed to be in one of an online state and a low-power state at runtime. In response to scaling down an application, at least one first application instance of the application in the online state is caused to enter the low-power state; and in response to scaling up the application, at least one second application instance of the application in the low-power state is caused to enter the online state. In this way, not only the cost of the application instance can be reduced, but also rapid elastic scaling can be performed on the application instance.

This application discloses a photo preview method, an electronic device, and a storage medium. The photo preview method includes: obtaining a photographing instruction, and taking a first photo according to the photographing instruction; displaying a floating thumbnail of the first photo in a photographing interface based on the first photo, the floating thumbnail having a floating effect; and scaling down the floating thumbnail to a stationary thumbnail if no image operation instruction is obtained in a display region of the floating thumbnail within a preset time period, or performing an operation on the floating thumbnail according to the image operation instruction if the image operation instruction is obtained in the display region of the floating thumbnail within the preset time period. According to the photo preview method, a photo preview effect can be significantly improved.

This invention discloses a method and system for simulating the consistency of droplet generator spreading characteristics between ground and space, used to obtain the spreading characteristics of droplet generators in a vacuum environment. It can equivalently convert ground test results into space test results, solving the problem of equivalent measurement for consistency between ground and space. By scaling down the operating parameters of the jet spreader under rated conditions, it overcomes the adverse effects caused by ground test environment limitations leading to excessively thick droplets that do not match the actual spreading effect in the space environment, thus achieving equivalent measurement for consistency between ground and space. Furthermore, by placing several droplet collectors with the same surface area and material at different locations and measuring the mass increment of multiple droplet collectors, it achieves measurement of liquid film thickness uniformity.

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 utility model discloses a three-phase voltage, frequency and phase sequence detection circuit, and belongs to the technical field of electric power detection. The three-phase voltage, frequency and phase sequence detection circuit comprises an MCU control unit, an A-phase detection circuit, a B-phase detection circuit and a C-phase detection circuit, wherein the A-phase detection circuit, the B-phase detection circuit and the C-phase detection circuit have the same structure. Each detection circuit comprises a voltage sampling circuit and a frequency detection circuit. According to the utility model, the operational amplifier is used for scaling down the input voltage, and the input voltage is skillfully raised to a range which can be accurately sampled by the MCU, compared with a traditional transformer detection method, the size and the weight are greatly reduced, the waveform distortion is extremely small, the characteristics of an original voltage signal are accurately reflected, and the detection precision is greatly improved. The effects of small size, light weight, low cost and high detection precision can be simultaneously achieved.

The invention discloses a device placement sequence management method and device, electronic equipment and a storage medium, and relates to the technical field of circuit board design. By selecting any one of same or similar devices needing to be checked, all same or similar devices in a component are automatically determined; the method comprises the following steps of: automatically screening a signal capable of judging a device serial number on a pin of each device, recording digital bit information of the signal, and putting the serial number generated according to the digital bit information and a device bit number into a block diagram which is scaled down with the design size of a component in an equal proportion, so that an engineer can accurately and comprehensively judge the arrangement sequence of the devices; and modification and adjustment are carried out as required according to the inspection result, so that the inspection efficiency is improved, a series of subsequent software and hardware design, production, test and later maintenance problems caused by wrong arrangement sequence of the devices can be avoided, a large amount of manpower and time cost is saved, and the product reliability is improved at the same time.

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.

The invention relates to a multi-network sharing management method, in particular to the technical field of computer networks, which integrates network resource types, selects equipment network node positions in a plurality of networks to set data acquisition points, checks whether original network data has errors and inconsistent information or not, and provides a multi-network sharing management method. The method comprises the following steps: reducing original network data to a specified range by using minimum-maximum normalization, mapping the original network data to the same scale, transmitting the processed network data to a database by using a wireless transmission mode, calling the network data in the database, and combining the network data with a timer to establish network time sequence data, the method comprises the following steps: acquiring a concentration trend, a dispersion degree and a distribution form of network time sequence data by utilizing statistical characteristics, predicting network data indexes through a time sequence model, acquiring high and low peak period network data indexes according to a time sequence model result, and performing multi-network scheduling management by utilizing a visual interface and dynamic selection.

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.

Re-layout processing of the one or more objects is executed when an instruction for a re-layout for borderless printing of one or more objects included in a page that can be printed by a printing apparatus is received. The re-layout processing includes at least processing to scale down a predetermined object that does not satisfy a predetermined condition relative to a background object so that, for objects other than the background object from among the one or more objects, an object that satisfies the predetermined condition is allowed to extend into, from among a printable region and an extend region in the page in a case of borderless printing, the extend region and an object that does not satisfy the predetermined condition is not allowed to extend into the extend region.

The invention provides a video data multi-scale down-sampling method and device based on an FPGA. The method comprises the following steps: counting line and field synchronization signals of received image data; according to the counting result of the row-field synchronizing signals, generating down-sampled row-field marking signals; enabling originally received image data to correspond to the generated down-sampling line field marking signal, carrying out image down-sampling, and writing the image into an FIFO (First In First Out); integrating the down-sampled image data into continuous line field image data by using FIFO (First In First Out), and outputting the continuous line field image data; and generating a new line field synchronization signal according to the down-sampled image data, aligning the new line field synchronization signal with the down-sampled line field image data, and synchronously outputting the line field synchronization signal and the down-sampled line field image data. According to the invention, multi-scale down-sampling of the video data based on the FPGA can be realized, down-sampling is carried out on a high-resolution image, the image resolution is reduced, the pressure of FPGA image processing is reduced, the sampling scale of down-sampling of the video data is variable, and setting of different sampling scales can be carried out according to actual needs.

The present invention provides a method for dynamically scaling the capacity of stateful services, including: 1. The coordination program collects the operating conditions of all server nodes, selects the server node with the lowest CPU usage rate for migration out, and selects the server node with the second lowest CPU usage rate for migration in; 2. The coordination program notifies the server node to be migrated out that it needs to scale down, and after the server node to be migrated out completes the scaling down, it sends a command indicating the completion of relocation to the coordination program; 3. After receiving the command indicating the completion of relocation of the server node to be migrated out, the coordination program sends a replacement instruction to the server node to be migrated in. After receiving the instruction, the server node to be migrated in loads the database data and starts processing the user requests forwarded by the server node to be migrated out; 4. Count the number of server nodes with a CPU usage rate and a memory usage rate higher than 80%. When the number of server nodes exceeds more than half of the total number of server nodes, select a server node from the standby pool to join the cluster. The present invention can dynamically scale the capacity without downtime for stateful services.

The invention relates to a method for developing drive, control, and / or regulation concepts for a vehicle using a vehicle model. In a method with which the development results can be tested under real conditions while simultaneously reducing development costs, a real vehicle model is scaled down to a true-to-scale size compared to the full-size vehicle to be developed. The dynamics of the full-size vehicle are mathematically determined, and the dynamics, rendered dimensionless using a similarity theory, are applied to the true-to-scale real vehicle model. The drive, control, and / or regulation concept is generated based on the dimensionless dynamics and tested on the scaled-down vehicle model. After a successful test, the concept is transferred to the full-size vehicle without adjustment.

This invention discloses a weight inference mapping method for optimizing memristor-based neural networks. First, the weight mapping parameter C is calculated based on the memristor conductance range and the weight range. t And the signal input transformation parameter θ; the weight matrix is ​​transformed according to the weight mapping parameter C t The signal is amplified proportionally and mapped onto a memristor array via an external circuit board. The voltage signal input to the memristor array is first scaled down proportionally according to the signal input conversion parameter θ, and then input into the mapped memristor array for matrix multiplication and addition. A constant resistor array is used to convert the current value output from the memristor array into a voltage signal. This voltage signal is added to the voltage signal at the next time step to obtain the input voltage signal for the next time step, thus initiating the weight inference mapping process for the next cycle. This method solves the problem of timing information loss during the weight inference mapping process, enabling RNN-type algorithms (such as GRU, LSTM, etc.) to be implemented entirely in hardware.