2 results about "Denormalization" patented technology

The application discloses a container throughput prediction method based on stacked ensemble learning, and relates to the technical field of intelligent ports. In the system operation, multi-source data is collected from a port operation system, an economic statistics platform and a shipping database, a comprehensive feature system containing throughput, freight rate, transportation time, policy variable, seasonal characteristics and macroeconomic indicators is constructed, and serialization and standardization processing are performed, and an improved model is constructed. The improved CNN-LSTM model introduces a deep separable convolution, a bidirectional LSTM and an improved attention mechanism to enhance the representation ability of key time steps, adopts Bayesian optimization to automatically search for hyperparameters, combines an early stopping strategy to control overfitting, simultaneously realizes multi-model integration based on inverse error weighting and meta-learning linear regression model, adaptively adjusts the rolling prediction window size according to the data coefficient of variation, generates future multi-period throughput prediction results through multi-step rolling prediction, and performs denormalization output.

This invention discloses a maneuvering target tracking method based on a Mamba tracking network, comprising: 1. Preprocessing trajectory data, calculating the mean and standard deviation, and normalizing the input data to eliminate sequence non-stationarity; 2. Constructing a maneuvering factor generator to map statistical features into adjustment factors reflecting the target's maneuvering characteristics; 3. Constructing a target tracking network based on a Mamba tracking network, extracting long-range spatiotemporal dependence features of the trajectory using a selective scanning mechanism, and dynamically adjusting the network feature response through adjustment factors; 4. Performing denormalization on the network output to recover the target state prediction value at the physical scale. This invention, through the organic combination of the Mamba tracking network and adjustment factors, effectively compensates for the loss of maneuvering information during the normalization process, significantly improving the accuracy and generalization ability of tracking highly maneuvering targets while maintaining linear computational complexity, and has significant application value.