10results about How to "Reduce reconstruction error" patented technology

A video encoding and decoding method, apparatus, and medium are provided. An example method of video decoding includes receiving a video bitstream including a plurality of frames, the plurality of frames including a current frame; and identifying a primary reference frame for context initialization of the current frame. The method further includes identifying a reference frame of the current frame based on the primary reference frame, and reconstructing the current frame by applying a cross component sample offset (CCSO) mode using the reference frame.

The application discloses a kind of space neighborhood reconstruction self-encoding multimodal fusion vector generation method, belong to geographic information science, computer vision and artificial intelligence cross technical field.The present mask self-encoding technique is only stopped at the technical limitation of grid level spatial unit, cannot process the heterogeneous modal vector of geographical entity with independent identity, the application proposes a kind of geographical entity-oriented multimodal mask self-encoding training framework: the multiple heterogeneous modal vectors of geographical entity with independent entity identification are randomly masked, the heterogeneous modal vector information of adjacent entity in the space neighborhood determined by k-ring algorithm is used, the masked modal is reconstructed by multimodal fusion network using the remaining modal vector of the entity not masked;With the weighted combination of reconstruction loss and contrast learning loss, the fusion network parameters are jointly optimized.The application also provides a joint optimization method and a cold start and gradual switching method.

This invention relates to the field of image analysis and discloses a method for analyzing railway freight car images. The method includes: acquiring an image of the freight car to be analyzed; reconstructing the freight car image using a trained reconstruction neural network to obtain a reconstructed image; calculating the reconstruction error between the reconstructed image and the freight car image; determining whether there are foreign objects in the freight car image based on the reconstruction error; identifying the foreign object region in the freight car image when foreign objects are present; querying the components to be detected in the freight car and locating the component region in the freight car image; performing fault detection on the component region to determine the fault type and key region of the faulty component; calculating the anomaly score of the key region; determining the abnormal region of the faulty component based on the anomaly score; and fusing information from the foreign object region, fault type, and abnormal region to obtain the analysis result of the freight car image. This invention can improve the real-time performance of railway freight car image analysis.

This invention discloses a generative recommendation system and method based on quantized vector retrieval and LLM, belonging to the technical field of computer information processing and artificial intelligence recommendation systems. The method includes: Step S1, extracting continuous collaborative features of users and items; Step S2, constructing and pre-training an AHPQ segmenter quantization module, mapping continuous collaborative features to discrete ID token sequences and optimizing the quantized codebook; Step S3, using the pre-trained codebook vector to collaboratively initialize the LLM's token embedding layer; Step S4, fine-tuning the LLM using a freeze-adapt strategy, and then generating user preference vectors through a collaborative semantic fusion module; Step S5, calculating the similarity between the user preference vector and the item database vector, and retrieving Top-K items based on cosine similarity as the recommendation result. This invention solves the problems of ID discretization difficulties and collaborative signal loss in LLM recommendation, preserving the high-order collaborative structure while leveraging the semantic reasoning capabilities of LLM, and simultaneously achieving efficient reasoning and cold-start generalization.

The application discloses a kind of based on LoRa's sea cable state remote wireless telemetry system and method, belong to cable laying state monitoring technical field, system includes being laid on several state monitoring nodes of sea cable, relay node and shipborne monitoring center.Adaptive different compensation strategies are taken according to the proportion of missing packet loss monitored by analysis computer: when mild packet loss, trend filling is executed using effective node change rate and spatial correlation;When moderate packet loss, confidence weight that attenuates dynamically with interruption time is introduced in reconstruction model;When severe packet loss, spatial physical correlation constraint matrix feature sub-block is extracted to perform order reduction calculation and combine spatial interpolation method to recover data.The application solves the problem of data loss caused by unstable wireless communication in extreme sea conditions through hierarchical compensation mechanism, ensuring that the full-line state distribution of sea cable can be reconstructed stably and accurately and the warning can be output under different packet loss conditions, significantly improving the robustness and construction safety of the sea cable monitoring system.

The application discloses a wind power daily scene generation method based on a tensor self-organizing mapping neural network, and comprises the following steps: obtaining a daily scene data set of multiple wind farms; clustering the daily scene data set by using a tensor self-organizing mapping neural network; constructing a variational autoencoder for each cluster respectively to extract hidden features from the daily scene data; performing random simulation and sampling on the daily scene data of each cluster by using the hidden features to obtain a daily scene hidden variable data set; decoding and reconstructing the daily scene hidden variable data set to obtain reconstructed daily scene data of each cluster; and aggregating the reconstructed daily scene data of each cluster to obtain a reconstructed daily scene data set. After clustering the daily scene samples, the application respectively reduces the dimension and reconstructs the samples of each cluster, improves the accuracy of the reconstructed daily scene data, significantly reduces the spatial and temporal correlation error of the reconstruction by considering the time and space correlation of the scene data, and strengthens the feature expression ability of the generated scene.

The invention discloses an ultrahigh time resolution flow field reconstruction method based on time sequence-space joint coding ViT, belongs to the field of fluid mechanics flow field prediction and reconstruction, and aims to solve the problems of insufficient time sequence feature modeling, limited key region precision and poor dynamic consistency of traditional flow field reconstruction. The method comprises the following steps: firstly, collecting multi-time-period sparse pressure field data, carrying out time sequence construction, and carrying out key region mask weighting preprocessing; constructing a ViT model fusing time sequence-space two-dimensional position coding, and cooperatively extracting time sequence dependence features and space local features of the pressure field; secondly, designing a multi-component loss function containing key region weighted MSE, flow field continuity constraint and structural similarity, and ensuring numerical value consistency, dynamic rationality and visual structural integrity of a reconstructed flow field; and finally, improving the high-frequency flow field feature capture capability through output layer optimization combining hierarchical attention aggregation and convolutional projection. According to the method, complete ultrahigh time resolution flow field data can be reconstructed, and the method is suitable for high-precision flow field prediction and engineering fluid mechanics analysis scenes.

This invention discloses a virtual skeleton perspective method, device, and storage medium, belonging to the field of medical image-assisted and computer vision-based 3D human body reconstruction technology. Addressing the shortcomings of existing medical imaging methods, such as the lack of visualization of the internal skeleton during positioning, poor real-time performance of iterative SKEL parameter solving, insufficient ability to measure real-world scales, and low reconstruction accuracy in occluded scenes, this invention replaces iterative optimization with a learned SKEL parameter estimation model, rapidly obtaining SKEL parameters through a single forward computation. The preferred scheme uses depth as a geometric prior to modulate attention, and adds bone length prediction to achieve metric scale recovery without internal parameters. Simultaneously, it realizes 3D skeleton virtual perspective overlay, positioning guidance, and real-world spatial distance measurement. This invention significantly improves the accuracy and efficiency of image positioning, reduces the radiation burden on patients, and balances reconstruction robustness and real-time performance.

The application discloses a kind of sparse sampling conditions based on optimization kriging full-band electromagnetic environment map construction method, belong to modern wireless communication network and cognitive electronic countermeasure field.First, the electromagnetic power received by each sparse sampling point in target area is obtained and converted into received signal strength, while the radiation source coordinates are obtained, the deterministic trend component of each sampling point is extracted using the logarithmic distance path loss model, and the residual component is further obtained;Then, combined with particle swarm optimization algorithm and leave-one-out cross-validation, the optimal core parameters of the semi-variogram function are obtained, and further combined with the residual component of the sampling point, the residual space interpolation of the target area grid is carried out;Finally, the interpolation result is superimposed with the deterministic trend component of the target area grid, and the full-band three-dimensional electromagnetic environment map of the target area is obtained.The application effectively breaks through the bottleneck of traditional interpolation algorithm ignoring the radio propagation law, and significantly improves the damage reconstruction robustness under the condition of extreme sparse sampling.

The application provides a differential absorption laser radar signal denoising method and a computer readable medium. The application performs multi-signal space-time average processing on a differential absorption laser radar measured signal sequence to obtain a space-time average laser radar signal sequence; acquires a to-be-decomposed radar signal sequence, obtains a plurality of to-be-decomposed radar signal forward and negative noise sequences through the to-be-decomposed radar signal sequence and a plurality of white noise sequences, and obtains a plurality of layer intrinsic mode signal sequences and a residual signal sequence through empirical mode decomposition calculation; divides the plurality of layer intrinsic mode signal sequences into each layer high-frequency and low-frequency intrinsic mode signal sequences, and respectively obtains reconstructed high-frequency and low-frequency intrinsic mode signal sequences through filtering processing; and obtains a denoised differential absorption laser radar signal sequence through residual signal sequence reconstruction calculation. The application can realize global adaptive filtering of a signal, and ensures that the signal has a high signal-to-noise ratio, a high space-time resolution, a low reconstruction error and a low distortion degree.