8results about How to "Reduce iterative process" patented technology

The invention relates to the technical field of clinical medical treatment, and discloses a disease deterioration risk identification method and system based on depth time sequence clustering enhancement, and the method comprises the steps: 1, obtaining multi-modal clinical sequence data of a patient, including physiological indexes of a time sequence, a laboratory detection result, historical diseases and medication data; 2, performing feature extraction and classification on the patient sequences by adopting a knowledge enhanced sequence clustering method, and grouping the patient sequences according to future outcome distribution of the patient sequences; 3, enabling the model to quickly adapt to a prediction task of a new patient subgroup through a meta-training process; and step 4, based on the trained meta-model, carrying out rapid adaptation on the new patient subtype, and predicting the possibility that the new patient subtype has a deterioration event in a certain time window in the future. The method and the system can effectively learn the disease change mode of the patient under the condition of limited clinical data, improve the prediction accuracy of the new patient subgroup, and are especially suitable for clinical prediction scenes under the condition of small samples.

This application relates to the field of automotive technology, and more particularly to a method, apparatus, device, and storage medium for adjusting a model. The method includes: obtaining a first collision simulation result based on a whole-vehicle collision model of a vehicle; the first collision simulation result includes at least the impact force of a first barrier and a protective beam in the vertical direction and a first acceleration of the non-deformed position of the protective beam in the vertical direction; determining the equivalent mass that generates the impact force based on the impact force and the first acceleration; controlling a second barrier included in the simplified collision model to impact the protective beam included in the simplified collision model with the impact force based on a simplified collision model of the vehicle and the equivalent mass, thereby obtaining a second collision simulation result; determining a first distance based on the first collision simulation result; determining a second distance based on the second collision simulation result; and adjusting the simplified collision model based on the difference between the first distance and the second distance.

The invention provides a CORDIC accelerator system and method for scaling factor compensation, and the system comprises an angle preprocessing module, an angle decoder module and a four-stage pipeline calculation module, and the angle preprocessing module maps an input angle Zin to a first angle interval, generating a corresponding reference angle selection signal, an initial angle and symbol information at the same time; the angle decoder module is connected with the output end of the angle preprocessing module and converts a reference angle selection signal into an initial horizontal coordinate and an initial vertical coordinate; and the four-stage assembly line calculation module is connected with the output end of the angle decoder module and the output end of the angle preprocessing module, performs four-stage iterative calculation based on 2 power series approximation according to the initial horizontal coordinate, the initial vertical coordinate, the initial angle and the symbol information, and outputs a final horizontal coordinate and a final vertical coordinate. According to the system and the application method thereof, the hardware resource consumption is reduced, and the calculation speed is greatly improved.

The invention belongs to the technical field of image automatic identification and processing, and discloses a diatom image automatic identification and water quality evaluation system, which comprises an image normalization engine, an identification engine and a residual quantization module, the image conversion module is used for converting an original microscope field-of-view image containing semantic interference into a normalized clean image; the recognition engine is connected with the image normalization engine in series and is used for only receiving normalized clean images for classification and counting, and the residual quantization module is connected with the image normalization engine in parallel and is used for receiving the residual map and carrying out quantitative statistics so as to realize evaluation of water quality physical indexes. According to the method, the semantic cleaning task and the standard recognition task are decoupled through a serial architecture, so that a standard recognition engine runs under an approximately ideal input condition, and the technical problem of recognition failure of a standard model caused by interferents in an actual field environment is solved.

This application belongs to the field of aero-engine design technology, specifically involving a method for predicting the axial force of an aero-engine rotor. Based on the parameters provided by the aero-engine aerodynamic scheme, the method constructs a simplified model of the turbine blade cooling flow characteristics, which can quickly and accurately predict and adjust the rotor axial force during the aero-engine scheme design stage, reduce the iteration of aero-engine design, and promote the development of aero-engines.

A method of estimating an extremum of an expectation value of a random field, the method executed on a computer and utilising a quantum computer. The quantum computer comprises a quantum circuit parameterised by a set of circuit parameters and configured to estimate an expectation value of a random variable of interest for the set of circuit parameters. The method uses an iterative optimisation algorithm to update the circuit parameters based on a sampling of the expectation value of a random variable of interest by the quantum computer in each iteration of the optimisation algorithm. The method utilises information related to the uncertainty of the estimates of the expectation values returned by the quantum computer to perform an optimal update of the circuit parameters in each iteration.

The present application provides a kind of particle swarm optimization method based on optimal loading contact area of planetary modified gear, mainly including the steps of establishing planetary gear comprehensive modified gear surface equation, calculating modified gear surface contact area and establishing optimization model and solving etc.The present application is based on particle swarm algorithm, and the tooth profile design of planetary gear mechanism is optimized to improve its gear surface contact area, and then improve carrying capacity.Particle swarm algorithm can provide efficient, accurate and easy-to-implement design method for the tooth profile modification of planetary mechanism, so as to improve the quality and performance of gear products, meet the demand of modern mechanical transmission system for high-performance gears.

The invention discloses a rigidity mass matching and structure optimization design method and system for an AC swing head. The method comprises the steps that S1, the AC swing head is divided into a plurality of substructures; s2, establishing a substructure dynamic model and extracting a stiffness matrix and a mass matrix of each substructure; s3, constructing an agent model of the stiffness matrix and the mass matrix of the substructure about stiffness and mass parameters of each structural member; s4, constructing an AC head swinging dynamic model; s5, identifying a weak structural member by adopting a sensitivity analysis method; s6, establishing a weak structural member multi-objective optimization model and solving an optimal sub-structure rigidity and mass matrix; s7, constructing a neural network agent model between the size parameters of the weak structural member and the stiffness / mass matrix of the substructure; and S8, taking the optimal sub-structure rigidity and mass matrix obtained in the step S6 as a target matrix, taking the size parameter of the weak structural member as a design variable, adopting an optimization algorithm, and realizing size optimization by minimizing the difference between the target matrix and the optimized matrix.