5results about How to "The solution accuracy is not high" patented technology

This invention discloses a method for locating epileptogenic zones based on high-frequency oscillations and connectivity, comprising the following steps: 1. Acquiring SEEG data and selecting representative channels; 2. Filtering to acquire 54-200Hz signals and selecting baseline and target data; 3. Acquiring normalized high-frequency energy; 4. Calculating time and energy coefficients to obtain a high-frequency epileptogenicity index; 5. Filtering to acquire 12-45Hz signals; 6. Calculating nonlinear regression analysis; 7. Calculating total intensity; 8. Defining and calculating the connectivity high-frequency epileptogenicity index and evaluating performance. This invention can accurately locate epileptogenic zones in patients with different seizure patterns, and has potential application value in epilepsy treatment.

This invention discloses a calibration method, apparatus, equipment, and medium for a fracture model of metallic materials. The method includes: determining initial fracture parameters and initial instability parameters of stress triaxiality to match calibration experiments; wherein the calibration experiments include at least one of uniaxial tensile tests, notched tensile tests, tension-shear tests, shear tests, compression tests, and biaxial tensile tests; each calibration experiment is matched with at least one stress triaxiality; updating the initial fracture parameters and initial instability parameters until both fracture parameters and instability parameters meet preset strain conditions; and determining the calibration result of the fracture model based on the fracture parameters and instability parameters of the stress triaxiality. This technical solution solves the problem of low accuracy in the calibration of fracture models of metallic materials, effectively improving calibration efficiency while increasing accuracy, and decoupling multi-condition simulations.

The present application relates to the technical field of unmanned driving, and discloses a vehicle control method and device, computer equipment and a storage medium, comprising: acquiring the pose of a target pallet to determine the target parking pose and reference line of a target vehicle; determining an angle parameter based on the reference line and a preset angle reference line, and determining a distance parameter of the target vehicle to the reference line based on the angle parameter and the current pose of the target vehicle; determining a path point on the reference line based on the distance parameter, the angle parameter and the target parking pose; controlling the target vehicle to move to the path point until the target vehicle reaches the target parking pose. The present application does not need to perform path planning in advance, reduces the time required for planning based on the target parking pose, improves overall efficiency, controls the vehicle based on the target parking pose, and realizes high-precision autonomous positioning of the vehicle depot distance driving.

This application discloses a method, apparatus, and computer program product for correcting genome assembly. The method includes: correcting the initial assembly result of a sample based on long-read sequencing data to obtain a first corrected result; correcting the first corrected result based on short-read sequencing data to obtain a second corrected result and a correction record; and filtering out erroneous corrections from the second corrected result based on the short-read sequencing data and the correction record to obtain the target assembly result of the sample. This application solves the problem in related technologies where the accuracy of the assembly result is low after first correcting the initial genome assembly result with long-read data and then with short-read data.

The application discloses a multi-intent determination method and device, a storage medium and an electronic device, relates to the technical field of smart homes, and comprises the following steps: performing data preprocessing on interaction information input by a target object to obtain preprocessed target interaction information; performing intent feature extraction on the target interaction information through a deep learning model to obtain a plurality of intent features corresponding to the target interaction information; determining the confidence of an intent recognition result corresponding to each intent feature through a target classifier, and determining a multi-intent recognition result according to the confidence of the intent recognition result corresponding to each intent feature; and determining an intent set corresponding to the interaction information in the multi-intent recognition result according to a logical relationship in the interaction information. By using the technical solution, the problem that the accuracy of an intent recognition technology is not high when the intent recognition technology processes multi-intents in a complex context in the related art is solved.