9results about How to "Efficient learning process" patented technology

The invention discloses a semi-supervised adaptive unstructured scene segmentation method, and relates to the technical field of computer vision. The method comprises the following steps: acquiring label-free image data of an unstructured scene; inputting the weakly enhanced image into a teacher model to generate a first prediction result; inputting the strong enhancement image into the student model to generate a second prediction result; calculating the JS divergence between the first prediction result and the second prediction result, and dynamically adjusting the attenuation coefficient of the index moving average according to the JS divergence; updating teacher model parameters according to the adjusted index moving average attenuation coefficient to obtain an unstructured scene segmentation model; and inputting a to-be-predicted unstructured scene image into the trained unstructured scene segmentation model, and outputting an unstructured scene segmentation result. According to the method, the teacher model weight updating rate is reduced in the model divergence significant stage such as the initial training stage or the difficult sample processing stage so as to suppress noise propagation, and the prediction precision of unstructured scene segmentation is effectively improved.

The application discloses a LIBS element quantitative analysis method based on double-branch feature fusion and electronic equipment, the method comprises the following steps: preprocessing the collected LIBS spectrum signal, including baseline correction and spectrum resampling; inputting the spectrum signal data after preprocessing into a double-branch feature extraction network, extracting local features through a CNN branch, and extracting global features through an MLP branch in parallel; performing adaptive weighted fusion on the local features and the global features through a gating fusion module to obtain fusion features; inputting the fusion features into a WMA-MLP model, the WMA-MLP model is based on MLP, integrates a multi-head self-attention mechanism and a residual module, is used for modeling the global dependency relationship between features, and outputs a final element quantitative analysis result. Through the parallelly arranged CNN and MLP branch feature extraction structures, more comprehensive spectrum feature representation can be obtained, and the accuracy and robustness of the spectrum analysis model are improved.

The application discloses a kind of brain-like reinforcement learning method and system based on layered experience playback, it is related to reinforcement learning and brain-like computing technical field.The method comprises the following steps: S1, collecting observation data and pre-processing;S2, initialize experience buffer pool and actor network, critic network and corresponding target network, parameter initialization;S3, initialize exploration noise and select action from actor network according to current state and execute, store the obtained experience sample to experience buffer pool;S4, obtain new sample from experience buffer pool, carry out short-term memory pool update;S5, use attention discrimination module to determine whether part of experience in short-term memory experience pool is transferred to long-term memory experience pool;S6, the parameters of actor network, critic network and corresponding target network are updated.The application uses the above method, improves the experience utilization rate of intelligent agent, improves the performance of reinforcement learning, and has wide application potential in many fields.

The present application relates to a kind of small sample disease classification methods based on association learning, comprising: first, build a small sample asphalt disease data set containing multiple asphalt disease types;Second, using the principle of association learning, develop a set of deep learning model and its training strategy suitable for small sample disease classification;Then, by setting "precision-speed" comprehensive evaluation index and a variety of damage simulation mode, evaluate the comprehensive performance and robustness of the model;Finally, the model is further adjusted and optimized, and deployed to road disease inspection equipment, realize the accurate classification of road disease.Compared with the prior art, the present application aims to solve the problem that traditional deep learning method excessively depends on training data size, alleviate the deficiency of existing method under small sample condition, improve the classification accuracy and system robustness, and provide a new solution for road disease detection and classification.

This invention proposes a robotic arm control method and apparatus. The method includes: constructing a simulated environment for robotic arm control; acquiring environmental state information, including the pose and velocity of the robotic arm and the position of the object to be manipulated; inputting the environmental state information as input to a symbolic network, the output of which is the robotic arm motion value, including the joint velocities of the robotic arm; selecting a suitable path from the symbolic network to generate a symbolic strategy; and deploying the robotic arm control task according to the symbolic strategy. This method can improve the efficiency of symbolic strategy learning, thereby learning robotic arm control with less interactive data and improving the accuracy of robotic arm control.

The invention relates to the technical field of smart homes, and discloses a defrosting detection method for an air conditioner, which comprises the following steps: acquiring real-time operation condition data of the air conditioner in a heating mode; different types of feature parameters in the real-time operation condition data are subjected to differential preprocessing, feature data after differential preprocessing are obtained, and differential preprocessing comprises feature explicit and feature implicit; inputting the preprocessed feature data into a neural network model to obtain a frosting degree prediction result; and defrosting control is conducted according to the frosting degree prediction result. Thus, the dynamic mode of the frosting state can be learned more accurately and efficiently, the detection precision, robustness and generalization ability under complex and changeable actual working conditions are remarkably improved, and real self-adaptive and high-reliability intelligent defrosting control is achieved. The invention further discloses a defrosting detection device for the air conditioner, the air conditioner and a computer readable storage medium.

The invention relates to a brain signal basic processing system and processing method for a multi-mode electroneurographic signal, and the system comprises an electroneurographic signal abnormity detection module which is configured to output a qualified multi-mode electroneurographic signal; the intracerebral and extracerebral unified spatial position coding module is configured to output a multi-modal electroneurographic signal with position codes; the dynamic brain region spatial convolution module is configured to perform adaptive spatial weighting on the multi-modal neuroelectric signals and convert the heterogeneous multi-channel neuroelectric signals into isomorphic single-channel feature sequences; and the composite self-supervised learning module is configured to execute self-supervised training combining mask prediction and autoregression prediction on the isomorphic single-channel feature sequence, and output a brain signal basic model after pre-training is completed, so as to complete downstream task evaluation based on the brain signal basic model after pre-training is completed. According to the method, the performance of multi-dimensional downstream tasks in the brain-computer interface field is improved, and the requirement of the downstream tasks for the data scale is reduced.

This invention discloses a multi-UAV task allocation and fusion method based on improved reinforcement learning, comprising: determining an initial parameter set for multi-UAV task allocation; fragmenting and decomposing multi-task objectives to determine cluster centers; using an adaptive clustering strategy based on center points to screen candidate clustering strategies and determine the optimal cluster set; determining a dynamic decay gradient strategy for the improved Q-learning network based on the action value function of basic reinforcement learning; determining a spiral cross-assignment mechanism for task redistribution based on a task redistribution mechanism with weighted coefficients; and determining a multi-UAV task allocation strategy based on the optimal cluster set of task points, the dynamic decay gradient strategy of the improved Q-learning network, and the task redistribution mechanism to complete the multi-UAV task allocation. This invention can effectively avoid unnecessary redundant allocation, improve the time consumption and range of task allocation, and increase the efficiency of task allocation.

The application relates to a dynamic gradient compression learning method of a federal learning system and belongs to the field of artificial intelligence. The method comprises the following steps: constructing a federal learning model, a server distributing different sampling communication bandwidths to clients, and the clients obtaining local models after training; considering resource constraints and gradient states of each client local model, calculating a pruning rate and a quantization level of a dynamic gradient compression strategy in this round; according to the pruning rate, each client performs a sparse operation on the gradient of the client to obtain a sparse gradient; according to the quantization level, each client performs a quantization and encoding operation on the sparse gradient to obtain compressed gradient data; each client uploads the compressed gradient data to the server, the server decompresses the gradient data to obtain decompressed gradient data; and the server aggregates the decompressed gradient data according to elements and updates a global model until the model converges. The application realizes efficient communication federal learning with better performance in a resource-limited scene.