7results about How to "Optimize network structure" patented technology

The invention relates to the technical field of high-performance polymer materials, in particular to low-dielectric and high-temperature-resistant modified cyanate ester resin and a preparation method thereof. Advantages and characteristics of bisphenol A type cyanate ester monomers and bisphenol M type cyanate ester monomers are combined, and the dielectric property and the thermal stability are remarkably improved. Diallyl bisphenol A is introduced as a network structure modifier, the phenolic hydroxyl group of the diallyl bisphenol A adjusts the curing reaction rate, and the allyl and bisphenol A structures of the diallyl bisphenol A finely regulate and control the rigid-flexible balance of a cross-linked network through chemical copolymerization, so that the curing process is controllable and the final network structure is optimized. Meanwhile, a toughening system is innovatively designed, epoxy-terminated liquid nitrile rubber is adopted for toughening, stable micron-scale rubber particles are formed in a resin matrix in a chemical bonding mode, and the fracture toughness is improved. The modified resin has excellent dielectric properties, high heat resistance and a wide process window, and meets the strict requirements of a new generation of low observability aircrafts on a bearing-stealth integrated composite material matrix.

The application relates to the field of ceramic materials, and particularly discloses high-thermal-conductivity alumina ceramics and a preparation process thereof. The high-thermal-conductivity alumina ceramics are prepared from 50-70 parts of alumina powder, 40-50 parts of alumina fiber, 8-15 parts of a sintering aid, 0.7-1.5 parts of a dispersant and 0.2-0.3 parts of a binder, wherein the sintering aid is beryllium-zirconium composite fiber. The high-thermal-conductivity alumina ceramics have excellent thermal conductivity and mechanical properties, and have important application prospects in the field of heat dissipation of high-performance electronic devices. In addition, the preparation process can reduce the sintering temperature of the alumina ceramics, form a more uniform and refined grain system in the alumina ceramics, and significantly improve the thermal conductivity of the alumina ceramics.

The application discloses a quad-rotor formation obstacle avoidance control method based on an improved DDPG algorithm, and belongs to the technical field of unmanned aerial vehicle formation control. The method adopts an improved DDPG reinforcement learning algorithm to plan an obstacle avoidance path of the quad-rotor. When the improved DDPG reinforcement learning algorithm is executed, the priority weight of each quadruple experience in the experience replay pool is initialized. After the quadruple experience in the experience replay pool is sampled and trained, the priority weight of each quadruple experience is recalculated based on a TD error, and a Sum_Tree structure is updated. The method effectively alleviates the training instability caused by hyperparameter sensitivity, the misleading of policy updating caused by overestimation of Q values, and the problem that key experiences are not sufficiently learned, accelerates the convergence speed of the quad-rotor formation obstacle avoidance training, and improves the obstacle avoidance effect.

The invention relates to the technical field of nondestructive testing and deep learning, in particular to a prefabricated concrete sheet member quality detection method based on lightweight Transform and GPR data. Comprising the steps of GPR image data input and preprocessing, lightweight convolutional neural network feature extraction, feature dimension reduction, position coding and serialization, Transform encoder global feature modeling, Transform decoder target query and detection, target classification and bounding box regression, post-processing and result output and the like. According to the method, the lightweight backbone network is adopted, the parameter quantity and the calculation quantity are reduced through the deep separable convolution, the inverse residual structure and the SE attention mechanism, and meanwhile, the detection precision of small targets and dense targets is improved; the problems that in the prior art, detection precision and calculation efficiency are difficult to consider at the same time, and a lightweight model is insufficient in small target and dense target detection capacity are solved.

The application discloses a preparation method of an alumina aerogel heat insulation felt, and comprises the following steps: adding an aluminum source precursor into a ketone substance to form a precursor solution through sufficient dissolution; adding an alcohol solvent containing a silicon source precursor, an amine substance and a catalyst into the precursor solution to form an alumina sol through uniform stirring; adding a surfactant into the alumina sol to form an alumina sol containing a light shielding agent through uniform stirring; adding light shielding agent particles into the alumina sol to form the alumina sol containing the light shielding agent; immersing a ceramic fiber felt into the alumina sol containing the light shielding agent to perform gel aging, so as to obtain a fiber felt / gel composite; and performing drying and heat treatment on the fiber felt / gel composite, so as to obtain the alumina aerogel heat insulation felt. The alumina aerogel heat insulation felt prepared by the method has the advantages of high temperature resistance, excellent heat insulation performance and good flexibility.

This invention relates to a conductive silver paste-specific glass powder for automotive rear window defroster lines, its preparation method, and its application. The glass powder comprises the following components in the indicated mass ratios: Bi₂O₃: 45-65%, SiO₂: 15-30%, B₂O₃: 5-12%, Al₂O₃: 1-5%, K₂O: 2-8%, Li₂O: 1-5%, CaO: 0.5-3%, and Sb₂O₃: 0.5-3%. It is processed according to the following steps: raw material selection, pretreatment, precise mixing, high-temperature melting, rapid water quenching, fine processing, and grading. Compared with existing technologies, this invention has advantages such as adaptability to high-temperature short-time sintering, high acid resistance, low sheet resistance, high welding strength, and high adhesion.

This application discloses a method for predicting harmful algal blooms based on Tent-GWO-GRU, comprising: acquiring and preprocessing historical water quality data of the target watershed; constructing a gated recurrent unit neural network (GRU) model based on the historical water quality data; optimizing the Grey Wolf Algorithm (GWO) based on the Tent chaotic mapping algorithm; and optimizing the GRU model parameters and making predictions. This application applies a time delay to the index data, fully considering the lag in algal bloom growth and evolution, thus improving the feasibility of the method. The introduction of the Tent chaotic mapping algorithm improves the method for generating the initial wolf pack in the GWO algorithm, resulting in a more uniform distribution of the initial wolf pack, significantly improving the algorithm's fitness value, and making it easier to find the global optimum. This application uses the Tent-GWO optimization algorithm to optimize the hyperparameters and network structure of the GRU model, which not only improves the model's stability but also achieves higher prediction accuracy.