6 results about "Coordinate descent method" patented technology

This invention discloses a latency optimization method for end-edge collaborative inference systems based on fluid antenna assistance (FA). The method first establishes an FA-assisted system model and constructs DNN partitioning collaboration, IFD transmission, and inference accuracy models based on the system model to determine the total inference latency, including local inference, IFD wireless transmission, and edge inference delays. Then, a joint optimization problem is constructed with the objective of minimizing the total inference latency, jointly optimizing parameters such as DNN partitioning points, transmit power of each device, and computational resource allocation. The joint optimization problem must satisfy inference accuracy and energy consumption constraints. Finally, the problem is decomposed into four sub-problems, and the block coordinate descent method is used to alternately optimize until convergence, completing the latency optimization of the end-edge collaborative inference system. This invention is the first to integrate FA and collaborative inference frameworks, achieving multi-dimensional parameter joint optimization, effectively reducing the total inference latency, and demonstrating significant performance advantages in scenarios with high accuracy thresholds and adverse channel conditions.

The invention discloses a current transformer error online monitoring method and system based on elastic network regression, and belongs to the technical field of current transformer error monitoring, and the method comprises the steps: collecting m groups of secondary fundamental current phasor values of n current transformers, and constructing an m * n-dimensional secondary current data matrix; establishing an n-dimensional overdetermined solution regression equation set based on the secondary current data matrix, the to-be-solved mutual inductor error parameter matrix and the residual vector; constructing an elastic network hybrid regularization loss function; solving the overdetermined regression equation set by adopting a coordinate descent method to obtain a mutual inductor error parameter matrix; and calculating the ratio difference and the angular difference of each mutual inductor according to the mutual inductor error parameter matrix. The technical problems that the error identification precision of an existing on-line monitoring device of the mutual inductor is not high enough, and multi-mutual inductor monitoring cannot be completed are solved.

The application provides a multi-view face recognition system (MvDRHSFS) based on sparse feature selection, which can select the view with the most information and the feature from multiple views of data through a projection matrix to learn a low-dimensional subspace, thereby solving the dimension disaster problem of high-dimensional face images. First, the F norm is used to constrain the projection matrix to select the view with the most information. Second, the l21 norm is used to constrain the projection matrix to select the feature with the most information. The projection matrix and the recovery matrix are optimized by the coordinate descent method to obtain the optimal solution, and the low-dimensional space representation of the test face is obtained through the projection matrix. The application makes up for the deficiency of the existing multi-view dimension reduction method which can only select a single view or feature, and can select the view with the most information and the feature at the same time, thereby better guiding the learning of the low-dimensional subspace of the face and further improving the performance of the face recognition system.

This invention discloses a motor design method based on a Kriging surrogate model. Specifically, it involves: collecting M combinations of motor size parameters as input sample data S, and using the corresponding motor performance as output sample data Y; forming a sample set from S and Y; training a Kriging surrogate model based on this sample set; constructing an optimization model with minimization as the objective function and the range of motor size parameter variations in the samples from which the Kriging surrogate model is trained as the feasible region; and solving the optimization model to obtain the motor parameters corresponding to the given motor performance. The method proposed in this invention improves the speed of solving the inverse mapping of the multi-performance Kriging surrogate model of a motor based on the coordinate descent method, and has good practicality and economy.

This invention discloses a multi-dimensional resource management method for UAV-assisted edge computing networks, primarily addressing the problem that existing UAVs equipped with edge computing servers cannot provide stable services to a large number of users. The implementation scheme is as follows: 1) Initialize network parameters; 2) Set constraints based on network characteristics; 3) Establish a minimum energy consumption problem P based on the initialized network parameters and constraints; 4) Determine the correlation matrix C, offloading matrix U, computational resource matrix, and UAV position matrix Z in problem P using the block coordinate descent method; 5) Perform multi-dimensional resource management based on the obtained C, U, F, and Z values. This invention can optimize offloading decisions and deployment schemes based on user location and computational task information, reducing system energy consumption and solving the problem of user service obstruction, while meeting the constraints of tolerable latency of computational tasks and the upper limit of UAV computational resources. It can be used for emergency communication and computing services in UAV-assisted edge computing networks under conditions of limited UAV energy consumption.

The invention provides a multi-model reasoning service-oriented cross-layer collaborative scheduling method, which comprises the following steps of: firstly, determining the number of sub-graphs and an initial division point, then iteratively optimizing sub-graph segmentation by using a coordinate descent method, and selecting an optimal scheme in combination with a historical request weight and an actual time delay. Then, the sub-graph is divided into sub-graph blocks in a recursive mode, parallel scheduling schemes of different batch processing sizes are generated, and online candidate strategies are screened out; and meanwhile, new establishment, stretching and splitting operations are configured to realize dynamic batch processing, so that an optimal parallel strategy is scheduled and selected on line based on the service quality. The invention further provides a multi-model reasoning service-oriented cross-layer collaborative scheduling system, a storage medium and computer equipment. Therefore, the online model service performance can be improved, the computing resource utilization rate can be improved, online scheduling and offline optimization cooperative scheduling can be realized, and the service performance can be greatly improved.