13 results about "Fine resolution" patented technology

The invention relates to a complex terrain air temperature refined downscaling method based on a TopoMW model, and belongs to the technical field of meteorological element downscaling, and the method comprises the steps: obtaining multi-source data of a target region, carrying out the preprocessing of the multi-source data, and fusing the multi-source data into a feature set; constructing a complex terrain multi-weighting matrix based on the preprocessed multi-source data; a TopoMW model improved based on UNet is established, a weighted MSE loss function is adopted to train the TopoMW model on the basis of a feature set in combination with a complex terrain multiple weighting matrix, an optimal downscaling model is obtained, and the contradiction between model statistic multiplexing and performance retention is solved; inputting a feature set to be predicted into the optimal downscaling model to obtain an initial prediction result; the initial prediction result is corrected based on the complex terrain multiple weighting matrix to obtain a final result, extreme deviation is eliminated, and accurate generation of the 30-meter high-resolution air temperature under the complex terrain is achieved.

Described herein are methods, systems and computer-program products for intra- and inter-gait classification for both symmetric and asymmetric gait patterns using images.

The invention particularly relates to a hierarchical search three-dimensional airspace path planning method and system based on an adaptive resolution grid. The method comprises the following steps: constructing a corresponding multi-resolution grid model based on a three-dimensional grid map; global path planning is carried out on the coarse resolution grids, coarse paths of all coarse grid nodes are determined, and a coarse path sequence is constructed; performing non-overlapping segmentation on the coarse path sequence; planning local windows matched with the fine-resolution grids, and determining window entrance and exit points corresponding to the local windows; performing obstacle recognition on each local window based on the fine resolution grid, calling a corresponding path generation strategy according to an obstacle recognition result of the local window, and determining a local refined sub-path corresponding to each local window; and splicing the path information of each local window in sequence to obtain a path planning result. According to the method, efficient and reliable path planning of a large-scale and complex three-dimensional airspace environment can be realized.

The application relates to a vector polygon rasterization method of a spherical hexagonal grid system and belongs to the technical field of geographic information. The vector polygon area to be rasterized is rasterized from coarse resolution to fine resolution by using the hierarchical characteristics of the grid system and adopting a cross-layer subdivision mode. The subdivision mode can generate a subunit by spacing a plurality of grid levels of a parent unit, solves the position uncertainty problem of the subunit relative to the parent unit after multiple times of subdivision of an adjacent level subdivision scheme, greatly reduces the number of units needing spatial relation judgment with the vector polygon, avoids the rasterization realized by traversing and judging the spatial relation of units one by one, and greatly improves the efficiency of the vector polygon rasterization on the spherical hexagonal grid system.

A tritium-compatible pellet gas gun for a deuterium-tritium (D-T) pellet fueling apparatus is provided. In one aspect, the tritium-compatible pellet gas gun includes a pellet sizer assembly that provides in situ adjustment of pellet length during injector operation. The pellet sizer assembly includes a guillotine slide that is actuated by a pusher tube, the guillotine slide being operable to restrict the orifice of the extruder nozzle to reduce pellet length with fine resolution. The tritium-compatible pellet gas gun also includes dual flexible metal bellows seals that maintain hermetic separation between the D-T fuel region, a guard vacuum, and the ambient environment, while still permitting linear motion. This configuration allows the pellet length to be reduced by up to 50% without venting or warming the D-T pellet fueling apparatus.

The invention relates to a remote sensing spatio-temporal data fusion method based on phenological constraint and meteorological driving, and belongs to the technical field of data processing. A high spatial resolution remote sensing image and a high time resolution remote sensing image are subjected to regression modeling, spatial filtering and residual error compensation, a fine resolution image and a coarse resolution image are subjected to preliminary time-space fusion, and a meteorological driving model between meteorological variable change and vegetation index short-term response is established; extracting a time sequence of a sample region from the high-time-resolution data, performing nonlinear curve fitting on the time sequence by using a Gaussian model, establishing a phenological growth model, performing parameter solution and optimization on the model to obtain a regional representative phenological model of the crop type, and carrying out phenological constraint and meteorological drive correction on the preliminary time-space fusion result to generate a final high-temporal-spatial-resolution image sequence. According to the method, a high-temporal-spatial-resolution remote sensing product which is lower in noise, more stable in phenological characteristic and more credible can be generated.

Systems and methods for configuring a hearing aid device may include automatically: selecting a sound file from a sound database, processing the sound file with a first set of parameter settings to generate two distinct coarse stimuli, playing the two distinct coarse stimuli to a user, recording a preferred coarse setting by the user, processing the sound file with a second set of parameter settings to generate two distinct shape stimuli, playing the two distinct shape stimuli to the user, recording a preferred shape setting by the user, processing the sound file with a third set of parameter settings to generate two distinct fine resolution stimuli, playing the two distinct fine resolution stimuli to the user, recording a preferred fine resolution setting by the user, and determining hearing aid parameter settings for the user based on the fine resolution setting.

Several embodiments include methods for performing hardware-assisted motion estimation while encoding blocks of a media image. A video encoder generates motion vector clues for multiple stages of motion estimation at progressively finer resolutions, culminating in the final motion estimation stage at the full resolution of the media image. The video encoder divides the media image into a grid of raster blocks, where the raster blocks are smaller than the standard encoding block size of the media image. At each stage, the video encoder generates a motion vector clue for each raster block at the selected resolution for that stage. For each motion vector clue, the video encoder generates forward-prediction clue data based on a reference media image that precedes the current media image.In addition, for each motion vector cue, the video encoder generates backward prediction cue data based on a reference media image that follows the current media image.

The invention discloses an anti-time-varying sensitive remote sensing image space-time fusion method and system, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a priori moment fine resolution image, a priori moment coarse resolution image and a prediction moment coarse resolution image; extracting multi-scale prior detail semantic features from the prior moment fine resolution image through a resolution reduction encoder; multi-scale semantic features are extracted from the coarse resolution images at the prior moment and the prediction moment through a super-resolution encoder, and time-varying semantic features are obtained through subtraction operation; inputting the two types of features into a double-orientation three-attention fusion decoder, and sequentially generating a preliminary local-global cross fusion feature and a decision fusion feature; the reconstruction unit reconstructs the decision fusion feature into a prediction moment fine resolution image; and performing an adversarial learning optimization generation process on the predicted image and the real image. According to the method, the prediction robustness of time-space fusion on time-varying information is improved.

This application relates to the field of X-ray detection technology, specifically to a high-throughput X-ray multi-energy spectral detection system and method. The detection method includes preparing n filters Z with different K absorption limits. i , i∈(1,n), n≥15; the incident X-rays, after collimation, pass through each filter Z i The electrical signal S was detected after attenuation. i S i =∫η×[φ(E)×C i (E)]dE,(E min E max ); for electrical signal S i The incident X-ray energy spectrum is obtained by discretizing the spectrum and then solving for the least squares solution. Based on the detection principle of energy integration, this application achieves flexible modulation and demodulation of high-throughput X-ray energy spectra through mathematical combinations of filters with different linear attenuation coefficients, balancing detection cost with obtaining high-resolution X-ray energy spectra.

This application relates to the field of remote sensing hydrological monitoring technology and discloses a method for estimating evapotranspiration based on data fusion and a physics-guided neural network. This method acquires multi-source satellite remote sensing and meteorological data, utilizes an enhanced spatiotemporal adaptive reflectance fusion model and a data mining sharing algorithm to fuse coarse and fine resolution images, generating daily seamless high-resolution surface reflectance and surface temperature data. A physics-guided neural network model is constructed, comprising a parametric subnetwork and a main network embedding the surface energy balance physical equations. The parametric subnetwork is used to predict the thermodynamic roughness length, driving the main network to iteratively calculate sensible and latent heat fluxes in conjunction with meteorological data. The model is trained based on a loss function containing physical constraints. This invention, through deep coupling of data-driven and physical mechanisms, resolves the spatiotemporal resolution contradiction of remote sensing data and improves the model's generalization ability and physical consistency in data-scarce regions.

The invention discloses a diffusion model space-time fusion method based on optical flow guidance. The method comprises the following steps: acquiring a fine spatial resolution image and a coarse spatial resolution image of the same observation area at a reference moment and a coarse spatial resolution image at a target moment; performing up-sampling on the coarse spatial resolution image to obtain an up-sampling image with the same scale as the fine spatial resolution image, and constructing a pseudo fine resolution image; estimating a pixel-level displacement optical flow for the fine spatial resolution image and the pseudo fine resolution image based on an optical flow estimation network to obtain a target scale optical flow; the gradient consistency of the fine spatial resolution image and the pseudo fine resolution image is calculated, and an optical flow confidence map is constructed; establishing a conditional diffusion model to obtain an initial fusion image at a target moment; and splicing the initial fusion image and the upsampled coarse spatial resolution image along the channel dimension, and outputting a high-resolution fusion image at the target moment. According to the method, the target moment high-resolution fusion image which is consistent in structure, clear in detail and good in radiation consistency can be generated.

This invention discloses a point cloud instance segmentation method and system based on coarse-to-fine adaptive clustering. First, it acquires LiDAR point cloud data of the target scene and obtains semantic segmentation labels using a semantic segmentation model, separating the point cloud into background and foreground object classes. A multi-level bird's-eye view BEV pyramid is constructed from coarse to fine resolution. At each resolution level, the clustering kernel size is adaptively adjusted based on the distance from the point cloud to the sensor for iterative clustering. Geometric center estimation and bounding box detection based on symmetry completion are performed on the candidate instances obtained from clustering. Oversegmentation and undersegmentation of candidate instances are geometrically verified using full coverage constraints and spatiotemporal mutual exclusion constraints. Undersegmented instances are passed to the next higher resolution level for processing, while oversegmented instances are fused. Finally, the background point cloud is combined to generate a panoramic segmentation result. This invention's method does not require additional training for instance segmentation, improving the accuracy and robustness of panoramic segmentation.