16results about How to "Effective study" patented technology

The application is a medical image segmentation method based on multi-modal self-supervision. First, the multi-modal medical image of the lesion tissue is obtained, including A-mode image and B-mode image, and the image is preprocessed. Then, a cycle-consistent modal contrast domain translation network is constructed, including two generators and two discriminators. The generator is used to convert the image of one mode into the image of another mode, including an encoder, an intermediate shared module and a decoder. The discriminator is used to judge the source of the input. Then, the cycle-consistent modal contrast domain translation network is pre-trained, the training loss is calculated, and the loss function includes multi-modal semantic consistency loss, adversarial loss, cross-domain translation loss and cycle consistency loss. Finally, the A-mode segmentation network and the B-mode segmentation network are constructed, the pre-trained weights are migrated to the two segmentation networks, and the trained two segmentation networks are respectively used for medical image segmentation of the corresponding mode. The contrast cross-domain translation is used as a multi-modal self-supervised pre-training task to learn more comprehensive modal features, promote the network to better learn modal characteristics and common knowledge, and improve the segmentation ability.

The application provides a traditional Chinese medicine efficacy-effect intelligent semantic alignment and reasoning method and device, and relates to the technical field of natural language processing. The method comprises the following steps: obtaining Chinese herbal medicine efficacy text and corresponding Chinese herbal medicine effect text, segmenting to obtain efficacy subword units and effect subword units, inputting into a fine-tuned BERT model to obtain word vectors; inputting into a double-layer bidirectional long short-term memory network to obtain efficacy multi-granularity features and effect multi-granularity features; inputting into a cross-attention layer to dynamically allocate matching weights between efficacy and effect, and classifying through a Softmax function to obtain predicted Chinese herbal medicine effect text. Through the fusion of the deep learning model, the accurate conversion of efficacy and effect is effectively realized, and new technical support is provided for the efficacy analysis in the field of traditional Chinese medicine.

This invention discloses a method for assisting in the determination of lesion severity using medical ultrasound images, comprising: preprocessing medical ultrasound images; constructing a deep neural network model, dividing the preprocessed ultrasound images into training, validation, and test sets; using the deep neural network model to extract features from the preprocessed medical ultrasound images to obtain feature maps, then performing feature enhancement on the feature maps to obtain feature-enhanced maps; training the deep neural network model using the feature-enhanced maps from the training set to obtain the parameters of the trained convolutional neural network model; validating the trained convolutional neural network model parameters using the feature-enhanced maps from the validation set, and testing the model using the test set; selecting parameters whose test accuracy meets the requirements as the final parameters of the deep neural network model; and using the deep neural network model obtained in step 2 to predict the severity of medical ultrasound images. This invention improves the accuracy of identification and reduces the workload of doctors.

The invention belongs to the technical field of deep learning, and discloses a CNN-XGBoost fusion airfoil aerodynamic coefficient prediction method based on a self-attention mechanism. The prediction method comprises the following steps: establishing an airfoil profile and an aerodynamic coefficient database of the airfoil profile; generating an airfoil geometric image; generating an airfoil grayscale image; establishing a prediction model; training and storing a prediction model; and airfoil aerodynamic coefficient prediction is carried out. According to the prediction method, an attention mechanism is introduced into a CNN and XGBoost is used for replacing an output layer in a CNN structure, so that the structure of a CNN airfoil aerodynamic coefficient prediction model is improved, the network performance is improved, airfoil image key feature learning is effectively carried out, and high-precision prediction of the airfoil aerodynamic coefficient under small sample data is realized.

The application provides a kind of teaching system and method based on VR virtual classroom, it is related to virtual reality technical field, the teaching system, comprising: acquisition unit, for obtaining the limb node coordinates of user;First identification unit, for obtaining the VR teaching action corresponding to VR limb guide model according to limb node coordinates, VR limb guide model and action arrangement;Execution unit, for executing VR teaching action by VR limb guide model to carry out teaching guidance;Second identification unit, for obtaining the actual action formed by the change of limb node coordinates in the process of VR teaching action teaching according to limb node coordinates;Comparison unit, for comparing actual action and VR teaching action, and obtaining comparison result;Generation unit, for generating the next teaching plan corresponding to action arrangement according to comparison result.The teaching system and method of the application can improve the learning enthusiasm and learning ability of user under the premise of ensuring the quality of teaching.

The invention discloses a wood defect detection method based on an improved YOLOv11 model. The method comprises the following steps: acquiring a surface image of wood to be detected; the to-be-detected wood surface image is input into an improved YOLOv11 model, a detection result is obtained, the improved YOLOv11 model completes feature extraction and fusion through a backbone network module, a neck module and a detection head module in sequence, and before the detection head module, the detection head module completes feature extraction and fusion of the to-be-detected wood surface image; a rectangular attention module, a texture removal attention module and a circular coordinate attention module are introduced in parallel, and reweighting is carried out on the feature maps of the corresponding hierarchies; and respectively sending the reweighted three layers of feature maps into the Detect detection heads in the corresponding detection heads, and outputting defect category and position information.

This invention belongs to the technical field of fault prediction and health management, and discloses a method and system for predicting the remaining useful life of mechanical equipment based on TWDBA-DCRN. The method includes the following steps: S1, collecting data on the equipment to be predicted from operation to failure, preprocessing and enhancing the data, labeling the remaining useful life of the data, and forming a database with a one-to-one correspondence between each window of data and its remaining useful life; S2, constructing a prediction model, training the prediction model using the data in the database to obtain a converged prediction model, and using this converged prediction model to predict the remaining useful life of the equipment to be predicted. This invention solves the problems of insufficient RTF data for mechanical equipment in RUL prediction, erroneous degradation information in noisy data, and low model prediction accuracy caused by gradient vanishing in deep time series networks.

The application discloses a feature automatic extraction method around a tooth boundary and a lesion recognition method, relates to the technical field of oral medicine, and comprises the following steps: acquiring a digital image of an oral X-ray apical film to be processed; performing automatic segmentation and contour extraction on a target tooth in the digital image of the oral X-ray apical film to obtain a tooth contour line of the target tooth; performing normalization processing on the tooth contour line; and traversing each boundary pixel point on the tooth contour line to generate an edge band feature map of the target tooth. The application can effectively extract local features highly related to lesions, especially features of a tooth boundary region, from the oral X-ray apical film, so that the recognition capability for early micro-lesions is improved.

This application provides a method, electronic device, storage medium, and product for updating parameters of a quantum circuit. The method includes: updating a first trainable parameter of a current noisy quantum circuit based on a current policy network to obtain at least one variation path of the first trainable parameter; determining a reward value based on each variation path and a reward function; the accuracy of the reward function and the output of the noisy quantum circuit is related to the degree of influence of noise on the output; updating a second trainable parameter of the policy network based on the reward value; and repeating the above steps until the policy network reaches a preset performance requirement. In this way, the policy network, through reinforcement learning, automatically adjusts the parameterized quantum gates in the noisy quantum circuit to achieve dynamic adaptation of the quantum circuit to noise. By dynamically adjusting the trainable parameters of the quantum circuit through a unified policy network, it is not necessary to model and train separately for each type of noise, thus improving processing efficiency.

Methods, computer systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting mRNA characteristics. The system obtains data representing a codon sequence of an mRNA molecule, generates an input token vector by numeric encoding the codon sequence, and generates an embedded feature vector by processing the input token vector using an embedded machine learning model having a first set of model parameters.

This invention provides an ECG signal classification system based on deep learning neural networks. It includes a common feature extraction module using a convolutional neural network (CNN) to extract shallow features from ECG signals; a domain-invariant branch module using a CNN to extract features from the shallow features and perform feature matching for the source domain by minimizing the classification loss, obtaining a prediction; a domain-specific branch module using a CNN with LMMD loss to perform feature matching between each pair of source and target domains for the input shallow features, obtaining n predictions; and a fusion module assigning different confidence levels to each sub-branch and combining the n+1 predictions to obtain the final prediction. The multi-branch network and multi-source unsupervised domain adaptation of this invention effectively learn the general features of ECG signals, enabling the knowledge learned from the source dataset to be applied to the unknown target dataset. Furthermore, by using a prior classifier-based fusion strategy, multiple predictions are organically combined to obtain the final result, further improving the model's generalization performance.

A self-adaptive sorting strategy optimization method based on artificial intelligence and a machine learning algorithm comprises the steps that 1, a sorting system based on multiple agents is established in a production workshop, and the sorting system comprises a plurality of auxiliary agents and a main agent located in the production workshop; 2, a reward function is constructed for each sorted sample, and dynamic weights are distributed for all indexes in the reward functions; and step 3, based on the currently established dynamic weight reward function, the slave agent carries out sorting by adopting a probability distribution-based hybrid exploration strategy. The method has the following beneficial effects: 1, the adaptability of the system is improved: by introducing sample quality stability indexes and dynamic weight distribution, the method can automatically adjust a sorting strategy to adapt to different production environments; 2, the sorting efficiency is optimized, an exploration strategy based on probability distribution and an exploration strategy based on counting are adopted, the intelligent agent can more effectively explore and learn the optimal sorting strategy, and the sorting efficiency is improved;

This disclosure relates to a method, apparatus, device, and medium for processing data stream requests. The method provided in the embodiments of this disclosure determines the data source based on the refresh type in the data stream request and pulls a candidate set from the data source. This achieves filtering of the data source through the refresh type, thereby ensuring that the data content in the candidate set pulled each time is different. At the same time, based on the state transition conditions satisfied by the key information of the candidate set, the refresh type is switched, so that when the server receives the next data stream request sent by the client, it pulls the candidate set again according to the refresh type after the switch. This does not take up too much browsing time for the operator and can ensure that the operator can effectively learn from the pushed data content, thus improving the operator's browsing experience.

The invention discloses a 6G edge network user prediction method based on fairness federation. The method comprises the following steps: step 1, initializing a 6G edge network user prediction mechanism based on fairness federation; 2, the edge core network completes local model updating according to a personalized enhancement strategy; 3, the central core network integrates knowledge to construct a global model according to a fairness aggregation strategy; 4, based on local model updating and a global model, training a 6G edge network user prediction mechanism based on fairness federation; and 5, performing 6G user prediction by using the trained 6G edge network user prediction mechanism based on the fairness federation. The method has the characteristics of personalized service enhancement, fair strategy implementation, prediction performance improvement and data privacy protection.

This invention provides an information recommendation method, apparatus, device, and storage medium. The method involves acquiring sequence feature data corresponding to information to be recommended, where the sequence feature data is collected when a user interacts with the information. The method then determines the category corresponding to the sequence feature data; determines the attention information corresponding to the sequence feature data for each category; determines the relevance information between the information to be recommended and the user based on the sequence feature data and the attention information corresponding to the category; and recommends the information to the user based on the relevance information. This invention, while ensuring sufficient cross-category interaction, avoids the risk of information intrusion between sequence feature data from different categories, thereby enabling more accurate modeling of user interests and improving the accuracy of the recommendation algorithm.

The application discloses a kind of mine limited environment image content inference enhanced quality evaluation method and system, first with the aid of visual language big model, unsupervisedly obtain the mine limited environment image content text description including key object, scene, attribute etc. in image, then the powerful reasoning ability of language big model and zero sample thinking chain technology is used to in-depth mining the relationship between image content and quality, after the reasoning relationship between image content and quality is condensed by language big model, text label for guiding model training is generated, and it is used as guide information to guide model to further mine the factors affecting image quality, finally only a small amount of samples are used to complete the final training of quality evaluation model.The application can make the model more effectively learn and in-depth mine the complex factors affecting image quality on the basis of small sample, and then improve the generalization ability and practicability of quality evaluation model, especially suitable for image quality evaluation work in mine environment.