22 results about "Dependent parameter" patented technology

A computer system that computes parameters associated with voxels in a sample is described. During operation, the computer system may obtain information specifying the MR measurements. Then, the computer system may determine an a priori regularizer using a pretrained neural network. For example, the a priori regularizer may correspond to a population of individuals. In some embodiments, the a priori regularizer may correspond to an average person in the population. Moreover, the computer system may compute the parameters based at least in part on the MR measurements, a model of sample physics and the a priori regularizer, where computing the parameters includes solving an inverse problem for the parameters based at least in part on the MR measurements.

The application relates to the technical field of system reliability analysis, in particular to a mixed Copula system reliability analysis method based on failure modes, which comprises the following steps: acquiring multi-failure mode degradation data, combining Bayesian inference to estimate parameters of a basic Copula function, rotating and transforming the basic Copula function to construct a mixed static Copula model, judging and optimizing the mixed static Copula model based on root mean square errors, extracting a time-varying correlation parameter sequence by using a sliding window, smoothing and denoising the time-varying correlation parameter sequence by using Kalman filtering, and constructing a mixed dynamic Copula model, and calculating the reliability of the system at different operation time points in combination with a failure mode limit state function. The application can represent the non-symmetrical tail correlation between the multiple failure modes and the time evolution law thereof, and improve the accuracy, stability and engineering applicability of system reliability analysis.

The application discloses a zero-pole matching discretization application method of a wave trap based on Taylor approximation, which comprises the following steps: determining the type and parameters of the wave trap, obtaining a wave trap transfer function and a transfer function parameter calculation formula according to the principle of the wave trap; discretizing the transfer function of the wave trap by using a zero-pole matching method to obtain an expression of the discrete transfer function of the wave trap; according to the expression of the discrete transfer function of the wave trap, related parameters are approximated by using a Taylor formula to obtain a simplified discrete transfer function, and complex operation is converted into basic operation; based on the implementation mode of the wave trap in a micro control unit, a difference equation is obtained through z inverse transformation, the discretized wave trap based on Taylor approximation is written into the micro control unit, and the effect of the wave trap is verified. The application realizes the complex transfer function of the wave trap, simplifies the calculation of the discrete transfer function, reduces the dependence on a mathematical function library, facilitates the setting of model parameters such as the wave trap, and improves the performance of a control system.

The invention discloses a Qos regulation and control method, device and equipment for a log input size threshold value, and the method comprises the steps: obtaining related parameters of different time points influencing the log input size threshold value, and inputting the related parameters as input vectors into a Transform; performing global time sensing and coding on the input vector by using an encoder to generate a context-related global feature vector, and performing dimensionality reduction and compression on the global feature vector by using a decoder to obtain an optimized feature vector; utilizing a first decision-making machine to decide and output the adjustment direction of the threshold value according to the optimized feature vector, and utilizing a second decision-making machine to decide and output the adjustment amplitude of the threshold value according to the optimized feature vector; and adjusting the current log input size threshold according to the output adjustment direction and the adjustment amplitude. Therefore, the problems that an existing dynamic regulation and control scheme of the log input size threshold is limited by the complexity of a manual writing algorithm, and personal use habits of a user cannot be considered are solved.

This invention provides a method and system for fitting device model parameters, relating to the field of semiconductor device modeling and parameter optimization technology. The method includes: acquiring initial information of a target device model, the initial information including parameters to be fitted; inputting the initial information into the target model to obtain fitting guidance parameters for the parameters to be fitted, the fitting guidance parameters being relevant parameters corresponding to each fitting stage, and the target model being a pre-trained large language model; after the fitting guidance parameters pass verification, fitting the parameters to be fitted at each fitting stage according to the fitting guidance parameters, obtaining the initial fitting result for each fitting stage; dynamically correcting the fitting guidance parameters according to the initial fitting result for each fitting stage to obtain corrected fitting guidance parameters; and fitting the parameters to be fitted at each fitting stage according to the corrected fitting guidance parameters to obtain the target fitting parameters for the parameters to be fitted.

Embodiments of the present disclosure provide a shared library-oriented fuzzer automatic generation method and device, which comprises: extracting an API function set requiring to be subjected to fuzz testing from source code and a header file of a target shared library; identifying control flow dependence and data flow dependence of an API interface based on the API function set, and constructing an API dependence graph; judging parameter types and deducing dependence relationships between parameters according to the API dependence graph, and distinguishing the parameters into independent variable parameters and state-dependent parameters; generating an executable fuzzer through combination scheduling based on function-to-function calling and dependence relationships in the API dependence graph, and embedding a LibFuzzer semantic coding structure into the fuzzer; and continuously tracking a coverage path during execution of the fuzzer, and preferentially selecting an unexecuted path for testing.

The invention relates to a method for screening key parameters in the production process of a hot-rolled coil plate, and belongs to the technical field of steel rolling optimization control methods. According to the technical scheme, key parameters are collected and sorted, and target parameters are analyzed and determined according to quality requirements; relevant parameters in the rolling process of the hot-rolled coil plate are collected to construct a data set; carrying out correlation analysis on all parameters in the data set and the selected target parameter, and obtaining a correlation coefficient; key parameters in the production process of the hot-rolled coil plate are screened, and feature engineering processing is conducted on a data set obtained after data arrangement; performing feature conversion on parameters belonging to category type data in the data set, and converting feature values into numeric type data; and performing parameter cross validation on the data set. The method has the beneficial effects that the parameters influencing the product quality can be quickly selected and modified, the difficulty of adjusting the production parameters is greatly reduced, the parameter adjusting speed is increased, and the product yield is remarkably increased.

This invention discloses a method for applying parameter perturbations during missile-aircraft separation, comprising: S1, extracting design variables affecting the separation trajectory of the aircraft and missile during separation; the design variables include first-type parameters, second-type parameters, and third-type parameters; the first-type parameters are parameters related to the initial state of the missile during separation, the second-type parameters are parameters related to the initial environment, and the third-type parameters are parameters related to missile body control; S2, based on the design variables in S1, selecting the parameters to be affected by the perturbation, and adding a small bias quantity with an artificially set distribution law to introduce the selected perturbation effect; the small bias quantity is a preset independent distribution function, or an actual value, or a distribution relationship measured by flight tests. The method for applying perturbation factors in missile-aircraft separation provided by this invention more closely reflects the real situation, providing designers with an effective method to examine the perturbation effects of parameters in the design.

The application discloses a fault probability monitoring and regulation system and method, relates to the technical field of fault monitoring, and collects to-be-determined related parameters in each period through a collection module, determines the optimal order of an autoregressive model of each related parameter by using the Akaike information criterion, and verifies the to-be-determined related parameters in each period, decides whether to perform secondary collection and replacement based on a verification result, generates a related parameter vector of each period, and starts a window translation along a period in a reverse order from the related parameter vector of each period through a monitoring module, calculates and arranges a comprehensive feature vector in each window, extracts and generates a core feature vector set through a time series self-encoder, trains a hidden Markov model by using a forward-backward algorithm, and obtains the fault probability of each period based on Bayesian inference, compares the fault probability with a probability threshold value to determine whether a fault exists, and executes regulation when the fault exists, so that high-precision fault monitoring and regulation based on collection self-checking, multi-dimensional data fusion and probability inference are realized.

The invention discloses a federated learning model training method based on distributed random convex difference optimization, and belongs to the technical field of federated learning model training, and the method specifically comprises the following steps: S1, system initialization; s2, according to the federated machine learning task selected in the S1 and the machine learning model, constructing a federated learning model training-oriented distributed optimization model with a convex difference structure; s3, the central server designs a solution architecture composed of external iteration and internal iteration and initializes relevant parameters required by the iteration process; s4, the client side executes local model parameter updating in parallel; s5, the central server executes global model parameter updating; and S6, the central server executes algorithm termination condition judgment and outputs optimal global model parameters. According to the federal learning model training method based on distributed random convex difference optimization, convergence is faster and more stable, and better generalization and interpretability can be obtained under non-convex regularization.

The application belongs to the technical field of data cleaning, and discloses a sensor drift data correction method for a nonlinear system for fault diagnosis, which comprises the following steps: step one, system state space models under conditions that sensors exist and do not exist measurement drift are respectively established, and relevant parameter setting and initialization are performed; step two, based on the model one, the system equation is linearized through Taylor expansion, prior estimation of performance parameters is performed, and prior estimation of performance parameters at the current time and prior estimation error variance are obtained; step three, a judgment criterion of measurement drift is constructed based on the prior estimation of performance parameters, and whether the current sensor appears measurement drift is judged; step four, according to the judgment result of measurement drift in the last step, different strategies are adopted to perform posterior estimation of performance parameters; and step five, fault diagnosis of the system is performed based on the posterior estimation of performance parameters at the current time. The application solves the problem of sensor measurement drift estimation in a space limited scene.

The invention relates to the technical field of data processing, in particular to a loom performance optimization method and system based on data acquisition, and the method comprises the following steps: collecting and setting weft density, rotating speed, actual weft density and power consumption, integrating the weft density, the rotating speed, the actual weft density and the power consumption into a synchronous parameter set, calculating correlation strength among parameters, and screening high-correlation parameters to form a feature sequence; parameters are set as independent variables, results are set as dependent variables, a quadratic regression model is input to construct a prediction model, and an optimal parameter combination is obtained by combining process target iterative optimization. According to the method, a parameter set containing a set value and a result value is constructed by collecting multi-dimensional operation data of the loom, internal relation quantification among parameters is achieved, significant influence factors are extracted through correlation screening, output reliability is improved through a regression prediction mechanism, global optimization is achieved through performance evaluation and iterative optimization under target constraint, experience one-sidedness is reduced, and the method is suitable for large-scale popularization and application. Energy consumption and efficiency balance is achieved, the operation stability and the resource utilization rate are improved, and production continuity and low cost are guaranteed.

The application discloses a method and system for underdetermined operational modal parameter identification based on signal change sliding window, which comprises the following steps: obtaining a structure vibration response signal; the number of sensor measuring points is less than the number of degrees of freedom; setting relevant parameters and letting the current sliding window serial number; performing modal parameter identification on the vibration response signal in the current window; calculating the change rate of the vibration response signal in the current window, changing the window length of the sliding window according to the change rate and moving to the next window, letting the current sliding window serial number; repeating the step until all the vibration response signals are identified; connecting all the identification results and outputting, and realizing underdetermined time-varying operational modal parameter identification. The application provides a tool for exploring and analyzing complex structures and dynamic characteristics in multi-dimensional time series data, which is suitable for processing a large amount of high-dimensional data with time-dependent characteristics, such as vehicle dynamics system, bridge system and the like.

In a wireless communication system, a method for operating a first device 100 is proposed. The method may include the steps of: establishing a PC5 RRC connection with a second device 200; transmitting, to the second device 200, terminal assistance information used to determine a SL DRX configuration, where the terminal assistance information is not allowed to be transmitted more than once; and receiving information related to the SL DRX configuration from the second device 200.

The invention relates to the technical field of fatigue strength evaluation of mechanical structures, and provides a universal model and method for predicting the fatigue life of a structure with defects, finite element models with defects are respectively established according to different defect information, and the fatigue life of the structure with the defects is solved based on fatigue analysis software; in combination with a Z parameter model, obtaining a Z parameter under each defect according to the established finite element model containing the defects; establishing linear correlation between the obtained fatigue life and a Z parameter, and fitting all obtained data points to obtain a fatigue life prediction formula; and calculating a Z parameter value according to actually detected defect information, and substituting the Z parameter value into a fatigue life prediction formula to complete fatigue life prediction. By expanding the applicability of the Z parameter to the whole service cycle of short service life and long service life and perfecting related parameters in the Z parameter, a universal model and method are provided for predicting the fatigue life of a structure containing defects.

The application provides an acquisition method and system for performance parameters of an aircraft engine and baseline values of the performance parameters, can automatically acquire real-time space-ground data link messages, classifies the messages according to key fields, reads all parameter fields in the messages and extracts relevant parameters for calculating EGTM and Baseline after determining the type of the messages, finally writes all the read parameters and the calculation results into a database, and the approximator based on the self-improved adaptive GA-ELM has good effects, the calculation precisions of EGTM and Baseline both meet the engine performance monitoring requirements, and the approximator based on the generalized regression network has reached high precision when approximating the deviation values of EGT, N2 and FF.

The invention belongs to the technical field of mining overlying strata damage intelligent prediction, and particularly relates to a coal seam overlying strata two-zone height machine learning joint prediction method, which specifically comprises the following steps of: acquiring relevant parameters and actually measured data of multiple mining area overlying strata two-zone heights, and storing the relevant parameters and the actually measured data into a table; importing related data, constructing a prediction model of a multi-machine learning algorithm, and evaluating the accuracy of the model; the algorithm with high accuracy is screened, and hyper-parameter optimization is carried out; combining the optimized single models to construct a combined prediction model; according to the method, multiple machine learning algorithms are fused to construct the joint prediction model, the advantages of different algorithms in processing complex data are fully played, compared with a single machine learning algorithm, the complex relation between the height of the two zones of the overlying strata of the coal seam and each influence factor can be more accurately captured, and the prediction accuracy is remarkably improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transferring heterogeneous state using generative models. One of the methods includes receiving a natural language description of parameters of a state of a first platform to be used to configure a state of a second platform; determining, using a receiver generative model, relevant parameters of the state of the second platform based on the natural language description of the parameters of the state of the first platform; and configuring the relevant parameters of the state of the second platform using the parameters of the state of the first platform.

The invention discloses a lower limb exoskeleton man-machine cooperation controller design method fusing human fuzzy decision, and relates to the technical field of man-machine interaction control, and the method comprises the steps: building a lower limb exoskeleton kinetic equation of a man-machine system containing parameter uncertainty and disturbance, taking an expected track as a constraint, and converting the expected track into a second-order form; providing a robust controller, and performing stability analysis on the robust controller; a cost functional is put forward, the cost functional is minimized, and an optimal fuzzy membership function is obtained to achieve man-machine cooperation under fuzzy decision; relevant parameters in the system are adjusted, and the effectiveness of the control method is analyzed; according to the cooperative strategy and control method, the human fuzzy decision and the mechanical system stability are combined, the performance of the mechanical system is regulated and controlled in real time through the human fuzzy decision so as to cope with different tasks and working conditions, and the controller has high robustness and adaptability and can be used for different control objects.

Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training. Some examples determine whether an operator has multiple corresponding NN modules within the translation dictionary and make an optimized selection.

The invention provides a global reliability sensitivity analysis method for a nonlinear structure system, and belongs to the technical field of system reliability analysis, and the method comprises the steps: S10, converting an input variable of an original reliability problem into a normal distribution space; step S20, constructing an important sampling density function according to a cross entropy important sampling principle; s30, sampling based on the important sampling density function to obtain a corresponding failure domain indicator function, constructing a state-related parameter model by using the failure domain indicator function and the input sample, obtaining conditional expectation based on first-order output of the state-related parameter model, and calculating a structure failure probability according to the structure failure probability and the conditional expectation, and finally, calculating global reliability sensitivity indexes of all input variables based on the structure failure probability. According to the method, the modeling sample size of small failure probability reliability problem state related parameter modeling can be remarkably reduced, and then the calculation cost of global reliability sensitivity analysis is effectively saved.

A method for constructing a component (102) comprises providing structure-related parameters; embedding a geometry using the provided structure-related parameters; and, based on the embedded geometry, providing a code (128) for performing a task for computer-aided design of the component (102). The invention makes it possible to apply machine learning methods directly to CAD data (e.g., B-Rep data). The predicted results are thus more accurate, and the application of ML methods becomes more practical.