5 results about "Support vector regression machine" patented technology

This invention relates to the field of vancomycin pharmacokinetic technology, specifically to the design of a vancomycin clearance prediction scheme based on a VPB ensemble model. This method, based on an ensemble learning strategy, constructs a blending ensemble model to predict vancomycin clearance in adult Chinese patients. First, a variational autoencoder is used to amplify the original sample data to increase data diversity. Then, a particle swarm optimization algorithm is introduced to optimize the parameters of multiple base learners, and the prediction results of the optimized base learners are used as new feature inputs. Finally, a support vector regression machine is used as a meta-learner to integrate and model the above features, forming the final vancomycin clearance prediction model. The VPB model constructed in this invention achieves a determination coefficient R² exceeding 0.9 on both the test and training sets, demonstrating superior prediction accuracy compared to population pharmacokinetic models.

The invention relates to a flexible machine finger instrument control method and system based on visual guidance, and relates to the technical field of robot control. Performing catheter tail end feature point segmentation of the lightweight convolutional neural network, and calling a PnP algorithm to solve a rotation variable and a translation variable under a camera coordinate system; three-dimensional coordinate system positioning information is called to serve as target track input, and contact type pressure sensing waveform data of the flexible machine finger and the catheter wall are output; synchronously loading original waveform data of a pressure sensor in the conduit, executing wavelet packet decomposition to extract frequency band energy distribution of dual-channel signals, and inputting detail coefficient components into a support vector regression machine for dimensionality reduction so as to obtain viscosity change rate and flow resistance gradient data of fluid in the conduit; and outputting a dredging control sequence of the vibration frequency, the amplitude and the action duration of the flexible machine finger. According to the invention, cooperative control of intelligent pre-judgment of the blockage risk and active dredging of the flexible machine finger is realized.

The application discloses a decoupling method of an integrated six-dimensional force sensor for a combine harvester and belongs to the technical field of sensor application. The sensor adopts a "Y" type three-beam radial symmetric structure, highly integrates a belt wheel function and a six-dimensional force detection function, establishes an elastic body structure simplified mechanics model, establishes a semi-analytical method model, analyzes and obtains strain distribution laws of X, Y and Z under single-axis load, and obtains a sensor geometric structure and a strain gauge measurement position meeting design requirements according to the strain distribution laws; a multi-output least square support vector regression machine is adopted, hyperparameters are optimized through a grid search, a high-precision mapping model of an electric signal to six-dimensional load is constructed, the decoupling precision is greatly improved, a whole-process innovation from structure to stress analysis and then to decoupling is realized, and the method is especially suitable for agricultural mechanical scenes such as a combine harvester and the like which are space-limited and have complex load.

This invention provides a method, apparatus, equipment, and medium for predicting shale gas content. The shale gas content prediction method includes the following steps: confirming the geological parameters of the target shale and performing grey relational analysis on the geological parameters and shale gas content to identify the main controlling factors of shale gas content; establishing a support vector regression (SVR) model, optimizing the parameters of the SVR model, outputting the optimal parameters, and constructing an optimal SVR model; training the optimal SVR model based on the main controlling factors of shale gas content to obtain the shale gas content prediction model; and predicting the gas content of the target shale based on the shale gas content prediction model. This invention uses grey relational analysis to select the main controlling factors and optimize the support vector regression (SVR) model, thus solving the problem that the performance of the SVR model depends on the selection of its hyperparameters.

The application discloses a new drilling potential evaluation method based on a mixed feature selection, and is characterized in that the number of newly produced wells in a historical interval is calculated, and reservoir development history data is marked; the correlation influence of each feature in the reservoir development history data is analyzed by using a Pearson product-moment correlation coefficient, individual fitness values are calculated, an elite selection mechanism is adopted to add individuals with larger fitness values and individuals generated after random selection and crossover to a sub-population, the population number is kept stable, and iteration is performed until an optimal feature combination of the oilfield history data is obtained; a support vector regression machine is used to mine deep information of the oilfield history data, and new drilling potential evaluation is realized. The application calculates the correlation influence of features by using the Pearson product-moment correlation coefficient, guides the selection of the optimal feature combination of the oilfield history data, speeds up the execution speed of the feature selection, adds the elite selection mechanism, increases the search precision of the feature combination of the oilfield history data, and improves the accuracy of the new drilling potential evaluation.