42results about How to "Optimize network parameters" patented technology

The embodiment of the present invention proposes a cognitive network-oriented performance optimization method and system. Through the joint optimization scheme of spectrum sensing, power allocation, and energy loss, the cooperation technology is adopted to effectively improve the cognitive network interruption performance and reduce the cognitive network performance. Outage probability, with the aim of improving the energy efficiency of cognitive networks. Wherein, the system implementing the method includes: a spectrum sensing module, an interruption performance optimization module, a transmission performance optimization module, and an energy consumption optimization module. In the energy consumption optimization module, through the statistical analysis of the energy in the cognitive network, as well as the distribution probability and energy estimation of the nodes, the optimal number of hops and the appropriate number of secondary users are selected to achieve the goal of optimizing network energy consumption.

This scheme discloses a multi-source ciphertext image retrieval method based on federated learning and secret sharing, which includes the following steps: S1. Based on federated learning, the image owner who joins the dual cloud platform is a federation member to perform convolutional neural network analysis on the dual cloud platform. Network for model training; S2. Authorized users complete ciphertext image retrieval based on additive secret sharing with the help of dual cloud platforms. This solution provides a multi-source ciphertext image retrieval solution based on federated learning and secret sharing, uses federated learning to simplify the neural network model structure used in retrieval, obtains better network parameters, and exchanges the cost of the image owner for better neural network parameters With a more streamlined network model structure, better convolutional neural networks can be used in ciphertext image retrieval, allowing users to get better and faster results when searching.

A rolling bearing fault diagnosis method with an improved model migration strategy belongs to the technical field of rolling bearing fault diagnosis. It is proposed to solve the problem that the data distribution of the same state in the source domain and the target domain are greatly different. Use wavelet transform to obtain the time-frequency spectrum of vibration signals of different types of bearings and construct an image dataset; select a certain type of data as the source domain, and other types of data as the target domain; use the source domain data to train the ResNet‑34 deep convolutional network to obtain the source Domain data classification model; use implicit gradient meta-learning to adaptively decide the transfer knowledge level and knowledge content to improve the model transfer strategy, avoiding the phenomenon that the gradient is not easy to converge in the heterogeneous architecture; introduce the transferred knowledge into the target domain ResNet‑152 convolution In the process of neural network data training, model migration is realized through parameter transfer; when training source domain and target domain networks, stochastic gradient descent algorithm is used to optimize network parameters, and fault diagnosis models of different types of rolling bearings are established.