14 results about "Adaptive integration" patented technology

The present invention provides system executing dynamic mixed reality experiences on user device including processors and non-transitory memory storing instruction receiving trigger signals from user interaction with trigger mechanisms through user devices. Trigger mechanisms comprise universal access links invoking instant applications without installation. System activates modular mixed reality engine dynamically loading mixed reality modules and digital assets, executing instant applications within sandboxed runtime environments using secure execution frameworks. System enables context-aware, dynamic, adaptive integration of virtual elements into physical environment displayed through user interfaces using real-time physical environment data and spatial analysis. Mixed reality experiences render on adaptive interactive user interfaces enabling dynamic digital placement, manipulation, transformation of virtual elements adjusted dynamically before and during rendering based on real-time physical environment data, spatial analysis, hardware-software configurations. System improves device functioning minimizing computational, overhead sandboxed execution, reducing launch latency via instant application invocation, enabling secure, adaptive, spatially consistent digital object interaction.

This invention provides a method and system for super-resolution reconstruction of low-quality images of mine tunneling faces. The method includes incorporating RSAB and SASSB modules into the HG structure. In SASSB, a semantic neighborhood scanning strategy is used to rearrange the feature sequences, and SASSM is used to recover the spatial structure at the hidden state level. In the MSMF module, local memories at each stage are aggregated using LMF, and GMF is used to adaptively integrate hierarchical memories of different depths. The system includes a shallow feature extraction module, a deep feature extraction module, and a reconstruction module. This invention simplifies computation and enables efficient combination of global and local information, while improving the ability to recover spatial structure and utilize hierarchical memory information.

The present application relates to the technical field of vegetation aboveground biomass prediction, in particular to a vegetation aboveground biomass prediction method based on multi-scale adaptive topology and cross-modal iterative interaction, aiming at solving the problem that remote sensing images and structured sample plot data are only simply spliced and insufficiently fused, resulting in insufficient prediction accuracy and generalization ability, the present application first collects multispectral remote sensing images and spatially registered sample plot attribute data of a target area, completes radiation correction, data enhancement and other preprocessing; adopts visual Transformer to extract image global semantic features, constructs an adaptive topology adjacency matrix for table data, obtains fine-grained features through a graph neural network, generates multi-scale graph representations through grouping and global aggregation, realizes double-modal multi-round iterative deep interaction through cross-attention, fuses and extracts global features for splicing, inputs a multi-regression head for parallel prediction, and outputs the final result through adaptive integration and prior knowledge calibration, thereby improving prediction accuracy and generalization.

This invention discloses an intelligent retry and traffic coordination control system and method for commercial APIs, belonging to the field of intelligent traffic coordination control technology. The system includes: a configuration management module for configuring independent rate limiters matching the official quotas of each API endpoint; a priority scheduling module for managing multi-priority blocking queues and background worker thread pools; an adaptive rate limiting module containing adaptive rate limiters corresponding one-to-one with API types; an exception handling module containing platform-specific exception handlers and error code mappers; a retry calculation module for calculating intelligent backoff time based on the number of retries and exception types; and a monitoring and statistics module for collecting operational metrics in real time, providing data for rate adjustment by the adaptive rate limiting module and dynamic priority adjustment by the priority scheduling module. This invention achieves efficient, stable, and adaptive integration capabilities for complex commercial API ecosystems through a multi-level intelligent control mechanism.

This invention relates to the field of short-term meteorological climate prediction, specifically disclosing a seasonal adaptive integration and dynamic anomaly correction temperature prediction system and method. The system includes a data acquisition module, a modeling and calculation module, and an evaluation and visualization module. The method includes: S1, fusing multi-source data to construct time-coded, lag, and cross-feature features, and generating a dynamic climate benchmark with variable weights; S2, based on the Stacking integration framework, using Ridge+LightGBM in winter, SVR and multinomial regression in summer, weighting during the transition season, and residual calibration in winter; S3, three-level anomaly evaluation, combined with benchmark calculation and visualization, with fine-tuning when the matching rate is low. The seasonal adaptive integration and dynamic anomaly correction temperature prediction system and method proposed in this invention solves the problems of difficulty in nonlinear capture, benchmark rigidity, and poor seasonal adaptation, effectively improving the accuracy of seasonal temperature prediction.

The application discloses a new energy power distribution network multi-resolution power prediction method considering source-load space correlation, relates to the power system power prediction technical field, and through adaptive variational modal decomposition based on source-load correlation guidance, decomposes the power signal into high-frequency, medium-frequency and low-frequency components, then constructs a multi-scale dynamic space correlation tensor, adopts a differentiated window to extract correlation characteristics for different frequency components, inputs the extracted correlation characteristics into a layered differentiated prediction model, trains by using a source-load collaborative constraint loss function, adjusts a fusion weight based on an adaptive integration mechanism of a prediction residual feedback, so as to realize power prediction, effectively solves the technical problems that signal decomposition is disconnected with a prediction task, correlation modeling is static and single, an integrated strategy lacks adaptive feedback, and a prediction model lacks physical constraints, and significantly improves the precision, adaptive capacity and robustness of new energy power distribution network power prediction.

The application discloses an arrhythmia detection method based on cross-modal data enhancement. In view of the problems of serious imbalance of class distribution of existing ECG data sets and limited effect of multi-modal feature fusion, the method comprises the following steps: db6 wavelet denoising and heartbeat segmentation are performed on the original electrocardiogram signal, and categories are merged according to the AAMI standard; a one-dimensional heartbeat time sequence signal is converted into a two-dimensional polyline waveform image with a pixel size of 224*224, and a metadata CSV file containing signal indexes, image paths and category labels is constructed; signal data and images are sequentially matched sample by sample, and a multi-modal paired data set is constructed; an intra-class multi-modal reorganization (ICMR) enhancement strategy is proposed, signal and image are independently and randomly sampled from the same category sample pool under the constraint of maintaining category consistency, and are re-paired, and the imbalance problem of categories is relieved through differential amplification rate; a double-flow multi-modal fusion classification network composed of a one-dimensional CNN-bidirectional LSTM signal encoder, a ResNet18 image encoder, a gating fusion module and a classification head is constructed, and two-way features of signal and image are adaptively integrated; cross-entropy loss function and Adam optimizer are used for end-to-end training and evaluation. The application effectively improves the recognition performance of the minority class arrhythmia.

The present application relates to a kind of target detection methods of point cloud density adaptive integration, belong to autonomous unmanned system environment perception technical field.The method modeling stage is based on three-dimensional columnar voxel division, the flux of beam and normalization density coefficient of voxel are calculated by laser radar parameter, and radar density model is established;While preset Kalman filtering parameter and correction association rule, construct kinematic correction model.Operation stage is first based on radar density model and carries out area screening, and adaptive integration is carried out to effective area, generates global integration point cloud;Second, feature extraction and target detection decoding are carried out to global integration point cloud by coding-decoding network, and output original detection result;Finally, target trajectory is established by matching continuous multiple frames detection result, and velocity is calculated using kinematic correction model and the shape of detection result is corrected.The present application enhances radar point cloud by adaptive integration, and improves the geometric accuracy of target by detection correction.

This invention discloses a medical image classification method and device based on Brownian distance covariance and statistical dependence. It designs a statistical dependence fusion DMF module, which enhances the overall distribution pattern of perceived features through covariance statistics, quantifies the statistical correlation between local and global features through a mutual information estimator, and focuses on lesion edges and complex texture areas through standard deviation spatial attention, achieving simultaneous spatial multi-scale fusion and statistical distribution perception. A dual-branch dynamic residual fusion framework is designed, introducing a Brownian distance covariance (BDC) branch to correct the prediction results. A dynamic weighting mechanism is designed based on prediction entropy, and a multi-level BDC progressive fusion strategy is designed, embedding BDC modules in multiple feature layers of the network. A learnable weighted fusion mechanism adaptively integrates multi-scale statistical information from local texture to global semantics, constructing a complete statistical dependence pyramid. Finally, the fused features are input to the decoder for decoding, generating classification results and achieving medical image classification.

This invention belongs to the field of urban computing and data mining technology, and specifically to a multi-view urban region embedding method based on spatial functional consistency. It includes the following steps: S1: multi-view data collection and processing; S2: spatial functional consistency in-view representation learning; S3: cross-view interaction within the region; S4: cross-view interaction between regions; S5: dual-feature attention fusion; S6: downstream task application. This invention effectively suppresses information interference caused by noisy edges and weakly correlated connections, improving the accuracy and stability of local structure modeling. Simultaneously, it enhances the global semantic connectivity and cross-view information propagation capabilities of the region representation. Furthermore, through a dual-layer attention fusion mechanism, it achieves adaptive integration of multi-view features and inter-regional correlation features, further improving the discriminativeness, robustness, and generalization ability of the final region embedding representation.

The application discloses a small sample automatic modulation recognition method for a complex channel environment, and fully excavates the complementarity of cross-view information by constructing a multi-view signal representation and using an independent feature encoder to learn discriminative features. On this basis, an adaptive measurement mechanism with intra-class variance perception is further introduced, and the feature dimension is dynamically reweighted according to the support set statistics, so as to suppress the unreliable dimension interference caused by noise and channel distortion. At the same time, a query-related multi-view distance attention fusion strategy is designed, and the measurement results of each view are adaptively integrated for different query samples, so as to avoid the negative transfer caused by fixed fusion. Then, the model is continuously learned on different small sample modulation recognition tasks, the loss function is optimized, the model is updated, and the optimal recognition model is obtained. Finally, the baseband signal belonging to a modulation type but the specific modulation type is unknown is input into the trained recognition model, and the modulation type is output. The application can effectively improve the small sample modulation recognition reliability in a complex wireless environment, provides a feasible and efficient solution for the modulation recognition application of an actual communication system, and provides a guarantee for subsequent demodulation and signal recovery.