13results about How to "Simplify the training process" patented technology

This invention provides a method for the germination and regeneration of mature rubber tree embryos into seedlings outside of test tubes, belonging to the field of seedling cultivation technology. The method uses somatic embryos derived from anther tissue culture as raw materials, and involves three stages of cultivation in a growth medium: dark culture, low-light culture, and strong-light culture. Then, the mature embryos that meet the requirements undergo closed-bottle / open-bottle acclimatization. After this acclimatization, they can be rooted and grown into seedlings in river sand outside the test tube. This method allows mature embryos to germinate and regenerate directly into seedlings outside the test tube, eliminating the need for a sterile culture room and specialized rooting medium, thus requiring less specialized knowledge from the operator. Furthermore, by combining the embryo germination and seedling regeneration with the in-test-tube and sand-bed acclimatization in one step, the cultivation process is simplified, the cultivation cycle is shortened by two months, and cultivation efficiency is improved while production costs are reduced.

The invention provides an oil-water separation effect prediction method and device based on a Lonion model. The method comprises the following steps: defining an input independent variable vector f (x) = [x1, x2,..., x8] T; performing feature engineering extension on the input independent variable vector to generate an extended feature vector f (z); the extended feature vector f (z) comprises an original feature vector, a polynomial feature vector and a physical interaction feature vector; inputting the extended feature vector f (z) into a pre-trained prediction model to obtain a predicted value, output by the pre-trained prediction model, of the oil content of the effluent of the air flotation synergistic device; inputting the predicted value into an integrated prediction framework to obtain a final predicted value and uncertainty estimation; displaying the final predicted value and the uncertainty estimation to a user through a display interface; according to the technical scheme, the accuracy, robustness and physical consistency of oil content prediction of the effluent can be improved.

The present application relates to the technical field of signal sparse representation, and specifically provides a separation type redundant dictionary learning algorithm for 3D signals, which comprises the following steps: S1: initialization process: initializing redundant dictionaries in three dimensions; S2: main iteration process: comprising a sparse coding stage and a dictionary updating stage; the sparse coding stage comprises: S21: reducing the sparse expression process to a two-dimensional matrix; S22: calculating the expression of samples in the sparse domain according to the initialized redundant dictionaries; S23: calculating the sparse expression error of all samples; the dictionary updating stage comprises: S24: arranging the 3D training sample data blocks into two-dimensional matrices after being respectively unfolded according to three dimensions; S25: updating the redundant dictionaries in three dimensions by solving a formula and performing atom normalization on the dictionaries. The learning algorithm in the present application reduces the time consumption of separation type dictionary training and reduces the sparse expression error of target signals.

The invention discloses a visual content generation method and a training method and device of a visual content generation model. The visual content generation method comprises the following steps: decoding discrete feature representation to obtain first visual data matched with target semantic content; obtaining a first continuous feature representation of the first visual data; and performing reconstruction processing on the first continuous feature representation through a visual content generation model to obtain a visual content generation result of the discrete feature representation. Compared with a method of directly performing reconstruction from discrete feature representation in related technologies, the method performs reconstruction from continuous feature representation, and the continuous feature representation contains richer spatial and semantic information, so that the complexity of executing a reconstruction process by the visual content generation model is remarkably reduced, and the reconstruction efficiency is improved. The efficiency of visual content generation based on discrete feature representation is improved.

The invention relates to a generation method, a processing method, a device, equipment, a storage medium and a program product, and the generation method comprises the steps: carrying out the feature extraction of the image content of a first image, obtaining a first feature map, and carrying out the feature extraction of the style content of a second image, obtaining a second feature map; performing alignment processing on the content features in the first feature map and the style features in the second feature map, and obtaining a first prediction image based on the aligned content features and style features; inputting the first image and the second image into a first model to obtain a second prediction image; wherein the first model is used for image color mapping; and on the basis of the difference information between the first prediction image and the second prediction image, the model parameters of the first model are updated until the target model is obtained, so that data set acquisition is simple, the difficulty of target model development is simplified, meanwhile, memory occupation is reduced, and the use performance of the electronic equipment is improved.

The application provides a WGAN-based FRP sheet and concrete interface bonding slip model generation method, which can automatically, quickly and accurately obtain the bonding slip model and has a wide application prospect. After the automatic and rapid acquisition of the bonding slip model, the bonding performance between the FRP sheet and the concrete can be accurately reflected, and a safe and reliable reinforcement component design calculation method can be established. The WGAN is used to predict the strain, so that the training process can be simplified and the training can be stable. The WGAN can replace the traditional experimental analysis to quickly and accurately establish a bonding strength model. The LSTM is used as the generator of the WGAN model, so that the gradient disappearance and explosion problems of the RNN are solved, and the strain data related to time can be more accurately predicted. The CNN is used as the discriminator of the WGAN model, so that the quality and convergence speed of the generated samples are improved.

The invention discloses a honeycomb-free large-scale MIMO unmanned aerial vehicle associated power control method and equipment based on reinforcement learning, and a medium, and relates to the technical field of wireless communication. The method comprises the following steps: acquiring parameters of a communication system containing multiple unmanned aerial vehicles and access points; a cellular-free large-scale MIMO transmission model is constructed; the method comprises the following steps: determining a state space, an action space and a reward function based on a cellular-free large-scale MIMO transmission model, and respectively constructing intelligent agents for an uplink power control coefficient optimization problem of an unmanned aerial vehicle in a system and an access point unmanned aerial vehicle clustering optimization problem; training the intelligent agent by using a depth deterministic strategy gradient reinforcement learning algorithm; and obtaining an uplink power control coefficient of the unmanned aerial vehicle in the system and an unmanned aerial vehicle clustering result of each access point by using the intelligent agent based on an optimized uplink power distribution strategy and an access point unmanned aerial vehicle clustering strategy. According to the method, efficient joint optimization of unmanned aerial vehicle access point association and uplink power control can be realized under a rapidly changing channel condition, and the method has relatively high practical practicability under a low-altitude economic background.

The application discloses a kind of based on metric and deep learning's radiation source radio frequency feature extraction method, it belongs to radiation source radio frequency feature extraction technical field.The present application solves the problem that it is difficult to extract RF feature with good stability and separability using existing method, and the extracted RF feature is easily disturbed by IM information and fails.The method of the present application is: constructing modeling dataset and ideal training dataset;Each RF signal sample in the constructed dataset is processed to obtain the processing result corresponding to each RF signal sample;The AE network built is trained using the processing result of the RF signal sample in the ideal training dataset;The FRM network is trained using the processing result of the RF signal sample in the modeling dataset and the trained AE network;After processing the RF signal to be detected, the processing result is input into the FRM network constrained by the signal feature encoder output by the trained, and the radiation source radio frequency feature extraction result is obtained.The present application can be applied to radiation source radio frequency feature extraction.

This invention provides a method and system for predicting multimodal drug interaction events based on compact representation learning. The method includes: acquiring multimodal features of each drug in a drug pair to be predicted, wherein the multimodal features include at least two of biological features, molecular structure features, and knowledge graph features; performing intramodal compact representation learning on each modal feature of each drug to eliminate redundant information within a single modality and generate compact sub-representations for each modality; using the compact sub-representations corresponding to the molecular structure features as anchors, performing cross-modal alignment of the compact sub-representations corresponding to the biological features and knowledge graph features through mutual information minimization constraints to eliminate intermodal redundant information; constructing a multimodal fusion representation for each drug based on the compact sub-representations of each modality; concatenating the multimodal fusion representations of the two drugs in the drug pair and inputting them into a classifier to output the prediction result of the interaction event type of the drug pair.

The application discloses a kind of train transmission motor, gear box and axle box fault diagnosis method and system, belong to train transmission system fault diagnosis technical field, method includes respectively designing special expert network for the key components of train transmission system;Introduce multi-head self-attention gate network, dynamically fuse the features output by special expert network;Adopt the joint loss function including cross-entropy loss, KL divergence regularization loss, load balancing loss, train special expert network and gate network;Based on teacher-student model architecture and knowledge distillation, realize new fault category adaptation and historical knowledge retention;The multi-component vibration signal to be diagnosed is input into the trained model, and the probability of the highest fault category is output as the diagnosis result through feature extraction, gate network fusion and classifier prediction.

The application discloses a three-dimensional point cloud few-shot classification method and system based on CLIP, which takes point cloud data and text description as original input, obtains multiple view images by projecting the point cloud at multiple angles, takes the multiple view images and the text description as the input of a pre-training model CLIP, obtains the corresponding text category of the multiple view images, and thus obtains the category of the original point cloud corresponding to the multiple view images. A learnable projection module is used to obtain several different optimal projection angles of the point cloud, and then a rotation matrix of the point cloud is obtained through the projection angle; secondly, a perspective projection method is used to obtain the projected multiple view two-dimensional images, and a ResNet network is used to extract the multiple view image features which can best reflect the object features, so that the classification is more simple and convenient; and the point cloud few-shot classification learning method has the advantages of simple training and strong universality.

The application discloses the technical field of plastic and additive identification, and relates to a plastic and additive identification method based on a bimodal multi-task neural network, which comprises the following steps: collecting plastic samples with known plastic base materials and additive contents; acquiring mid-infrared hyperspectral data and X-ray fluorescence spectral data of the samples based on a mid-infrared hyperspectral camera and a handheld X-ray fluorescence spectrometer; obtaining the plastic base material type, the additive type and the additive dose of the samples based on sample formulations or chemical analysis results; and determining the type and regression label of the plastic base material type, the additive type and the additive dose. The application is based on the natural complementarity of mid-infrared spectra and X-ray fluorescence spectra in the aspects of molecular structure information and element composition information, and is oriented towards the spectral characteristics of the plastic-additive system. The target identification model can improve the analysis capability for complex waste plastic systems carrying multiple additives, and has outstanding uniqueness and engineering application value.

The present application relates to the technical field of robot motion control, and especially relates to a kind of quadruped robot reinforcement learning motion controller training, simulation, deployment method based on elevation map, the method constructs the asymmetric Actor-Critic deep reinforcement learning model based on double-estimator hybrid explicit and implicit feature state estimation, and the body state estimator and the terrain state estimator are respectively extracted body perception implicit feature and foot end terrain perception implicit feature;Build multiple simulation training terrains and design course teaching mechanism and multiple types of reward functions for training;Respectively build simulation verification environment based on MuJoCo and Gazebo, construct local elevation map by ray detection or laser radar point cloud fusion, and splice as the strategy network input with the body state of robot, the present application effectively improves the motion stability, environmental adaptability and simulation to reality migration reliability of quadruped robot in complex terrain.