10results about How to "Achieve generalization" patented technology

The application relates to the field of battery life prediction, in particular to a multi-feature fuel cell life prediction method based on a long short-term memory network; the method comprises the following steps: collecting voltage attenuation data and collecting running state data to construct an original input data set; analyzing the running state data, calculating a correlation coefficient according to the voltage attenuation data and each running state data, analyzing the correlation coefficient, and obtaining an input feature set; processing the input feature set and the voltage attenuation data to construct a neural network training data structure; dividing the training data structure into a training set and a test set; constructing a network architecture solution space to be searched, analyzing the network architecture solution space, constructing a fitness function, and determining an optimal network architecture parameter combination based on the fitness function; and inputting the training set into the optimal network architecture parameter combination for training to generate a target detection prediction result. The application can improve the prediction accuracy of the voltage attenuation trend.

This invention provides an enzyme turnover rate prediction method based on a dual-route hybrid expert mechanism, belonging to the field of bioinformatics technology. It solves the problems of low data utilization, insufficient information mining, and poor robustness in existing prediction methods due to missing values ​​and temperatures. The technical solution includes the following steps: S1: Constructing a unified standard enzyme turnover rate dataset; S2: Extracting multimodal embedding features; S3: Constructing intra- / inter-modal encoders; S4: Combining modalities into an expert hybrid module; S5: Designing an attention fusion mechanism. This invention can achieve high-precision prediction under complex in vitro environmental conditions.

The utility model discloses a kind of controller upgrading system supporting multiple vehicle-mounted communication protocol, including microcontroller, man-machine interactive device, transceiver and power device;The microcontroller is electrically connected with man-machine interactive device and transceiver respectively, and the power device is electrically connected with microcontroller;Integrated controller and communication interface unit are equipped on the microcontroller;The integrated controller is electrically connected with transceiver, and the communication interface unit includes data storage interface, peripheral debugging interface and external upgrading interface;The integrated controller includes multi-protocol communication module, storage module, power management module;The power management module is electrically connected with power device;The storage module is electrically connected with data storage interface;Multi-protocol communication module is electrically connected with external upgrading interface, and the peripheral debugging interface is connected with external equipment.

The present application relates to the technical field of industrial automation detection, and particularly discloses a kind of vision detection system of foreign matter in tank, the system includes rotary positioning module, multi-source lighting module, multi-modal imaging module, image processing decision module and visual interface module;The present scheme integrates annular LED bright field light source, lateral dark field light source and polarized light source, dynamically switches illumination mode according to tank body material, cooperates with high-speed camera array to synchronously collect images from top-down and lateral oblique angle, realizes all-around coverage to tank wall, tank bottom, liquid level and the like;Pre-trained DINO model is used to extract multi-level visual token, all sequences are uniformly modeled using lightweight Mamba autoregressive model, effectively capturing the global dependence and local details of the spatial structure inside the tank, while introducing a dynamic anomaly threshold mechanism, effectively avoiding the problem of missing small foreign matter caused by image down-sampling.

The application discloses a product recommendation network hyperparameter optimization method and device and electronic equipment. The method comprises the following steps: obtaining a plurality of hyperparameter spaces of a plurality of product recommendation networks from historical tuning logs based on an LLM; clustering the hyperparameter spaces according to the outputs of the corresponding proxy models of the product recommendation networks; taking the plurality of clustered hyperparameter spaces and the corresponding cluster-level evaluation scores as sample data; enhancing the LLM based on a low-rank adaptive Lora module and the sample data to obtain an enhanced LLM; weighting the hyperparameter prediction value and the space exploration value of each candidate hyperparameter cluster in a plurality of candidate hyperparameter clusters based on the proxy model of the product recommendation network to be optimized to obtain the intra-cluster prediction score of the candidate hyperparameter cluster; and determining the sampling proportion of each candidate hyperparameter cluster based on the intra-cluster prediction score of the candidate hyperparameter cluster through the enhanced LLM to obtain the optimized hyperparameters of the product recommendation network.

A multi-task prompt decision transformer construction method and device, equipment and storage medium, relate to the field of robot control simulation environment, including constructing MPDT, MPDT includes multiple decision maker layers and triple prompt module constructed based on reward, state and action;Control target decision maker layer and triple prompt module are in active state, MPDT is trained based on offline training sample, and multi-task prompt fine-tuning is carried out based on triple prompt, specific task prompt and cross-task prompt are generated;After training is completed, the combination of cross-task prompt and test sample is used as the input of MPDT for testing, the hidden state test mean and hidden state test variance corresponding to the test sample are calculated, and the mean and variance of the offline training sample are aligned and calculated;Based on the alignment result, the cross-task prompt is updated, the target multi-task prompt decision transformer is generated, and the generalization ability of the transformer in the zero sample and multi-task scene is improved.

The invention relates to a rare disease identification method and system based on multi-scale vision-language prompt multi-instance learning, and the method faces a full-view digital pathological image (WSI), and introduces the prior knowledge of the pathological field under the weak supervision condition of only using a slice-level label, so as to identify the rare disease. Constructing a multi-scale vision-language collaborative prompt mechanism: generating descriptive text prompts corresponding to image blocks of different scales by utilizing a large language model, designing a prototype-guided image block decoder to aggregate massive image block features, and meanwhile, realizing cross-modal alignment in combination with a context-guided text decoder; the method simulates the diagnosis process of a pathologist, achieves high-precision classification under small samples, remarkably improves the recognition accuracy of rare diseases, has good cross-center generalization ability, supports interpretable evidence output, does not need large-scale data labeling or de novo pre-training, and facilitates clinical rapid deployment.

The application discloses a multi-fingered hand operation reinforcement learning method based on a human motion prediction model and belongs to the field of humanoid robot dexterous hand body operation. A human motion prediction model is trained by using data with human hand and object three-dimensional pose annotation, which is used to generate three-dimensional trajectories of human hand and object interaction in a generalizable manner. A general reward function is constructed by using the trajectory, which is used to train a dexterous hand reinforcement learning strategy in simulation. Then, the migration ability of the strategy from simulation to reality is improved through simulation parameter domain randomization and curriculum learning, and the trained reinforcement learning strategy is deployed on a real-world robot. The scheme introduces the understanding of the future pose of the object to be operated, predicts the interaction process of the human hand and the object as a whole, and constructs a unified, simple, efficient reward function based on this, which is suitable for different operation tasks and different types of dexterous hands, and has the advantages of strong generalization, high success rate and strong reward function universality.

The invention discloses construction of a petroleum yield prediction model for domain knowledge migration in a multi-reservoir scene, and belongs to the technical field of petroleum engineering and intelligent prediction. According to the method, a domain knowledge migration mechanism is introduced, so that the model can share depth feature information among multiple oil reservoirs, the prediction precision and model robustness of small sample oil reservoirs are effectively improved, and the problems of poor model generalization, high training cost, unstable prediction and the like in a traditional method are solved. The method is clear in structure and moderate in calculation amount, has good interpretability and visualization ability, and can be widely applied to the fields of oil reservoir yield prediction, intelligent oil field management, production optimization decision making and the like.

A computerized method has the steps of generating an input computational graph (CG) for representing a neural architecture, and performing a plurality of optimization steps in at least one iteration to obtain a modified neural architecture represented by the modified CG, thereby obtaining a neural network for use in one or more computing devices. The optimization step includes identifying one or more sub-graphs in the input CG, obtaining the modified CG by replacing the identified one or more sub-graphs with one or more replacement sub-graphs, respectively, evaluating one or more indicators of the modified neural network architecture represented by the modified CG, and, in accordance with the evaluation, modifying the one or more indicators of the modified neural network architecture. And selecting the modified CG or the input CG. When the optimization step is performed in multiple iterations, the selected CG obtained in one iteration is used as the input CG in the next iteration.