4results about How to "Increase feature dimension" patented technology

The application discloses a sea surface small target detection method based on multi-domain multi-dimensional feature combination, and adaptively extracts deep features of sea clutter and target echo signals by using a stack sparse auto-encoder, so that the feature dimension is improved to ensure the feature feasibility. Since the feature extraction is a complete adaptive process, the complexity of the model is reduced. Meanwhile, aiming at the problem that the feature distinction degree of the sea clutter and target echo data in a single domain is low, a time-frequency domain feature combination method is provided to improve the feature difference and ensure the stable and efficient detection performance of the detector. Through an adaptive genetic algorithm, the convergence process of the super parameter group optimization is accelerated, and the local optimization of the super parameter is prevented to a certain extent, so that the final detection probability is effectively improved. The experimental results show that the detector provided by the application has better detection effect on high sea state data, the detection probability is improved by 27.6%, and the sea surface small target detection under high sea state can be coped with.

This invention relates to the field of deep learning technology, specifically providing a model training method, control device, and computer-readable storage medium, aiming to solve the problem of ensuring model evaluation performance while ensuring rapid model transfer. To this end, the invention obtains a trained first linear model, and creates a second linear model based on the first linear model, wherein the feature dimension of the second linear model is greater than that of the first linear model. Features are padded on a first dataset used to train the first linear model, and the padded first and second datasets are used to train the second linear model, resulting in a trained second linear model. This allows for rapid transfer based on the first linear model, and the second linear model, by using a dataset with more feature fields, achieves higher performance with higher feature dimensions.

The application provides a proppant migration state prediction method and system based on images, and relates to the technical field of oil and gas development.The method comprises the following steps: processing and predicting a real-time image sequence currently collected by using a prediction neural network model to obtain current placement form prediction data of the proppant in a complex fracture; obtaining a regulation instruction based on the current placement form prediction data, and sending the regulation instruction to a field fracturing construction control unit to close-loop control the proppant migration process.The application realizes accurate prediction and closed-loop control of the proppant migration state through downhole image collection, preprocessing, segmentation correction, multi-source data fusion modeling and real-time prediction and regulation.

This invention discloses a method for dynamic filtering and concentration prediction of drug extraction time-series signals, belonging to the field of drug signal detection technology. The method includes acquiring an original time-series signal sequence containing subsequences of pressure fluctuations, temperature drift, and conductivity changes. Dynamic noise features are extracted to separate the periodic vibration noise component of the equipment from the random noise component of fluid turbulence. An adaptive filtering kernel function is constructed and convolutionally processed to obtain the initial filtered signal. Baseline drift is corrected using a weighted baseline estimate based on the temperature drift subsequence, resulting in a dynamically corrected signal sequence. Signal feature inflection points are detected and marked, and single extraction cycle segments are segmented. Peak amplitude sequences and inter-peak time interval sequences are extracted and input into a pre-trained model to output predicted drug concentration values. This method can adaptively adapt to complex noise environments, correct signal baseline shifts, accurately divide extraction cycles, effectively mine time-series signal features, and optimize drug concentration prediction performance.