8results about How to "Fast training" patented technology

The application provides a camera scheduling model training method, a violation event detection method and device. The camera scheduling model training method comprises: obtaining historical sample data of a camera; constructing a first sample probability distribution based on the historical sample data; sampling based on the first sample probability distribution to obtain first sample action data; and training a reinforcement learning model based on the first sample action data to obtain a camera scheduling model. The application uses historical sample data to construct a sample probability distribution, and then uses the sample probability distribution for sampling as the input of the reinforcement learning model to train the reinforcement learning model. Since the input of the reinforcement learning model is generated by the constructed sample probability distribution and does not truly respond to the environment, the problem of long time consumption for obtaining real samples is solved, a large number of samples can be quickly obtained for training, and the training efficiency of the camera scheduling model is improved.

This invention discloses a substation acoustic signature fault diagnosis method based on LSTM-BHPSO. The method involves collecting substation acoustic signature signals and extracting features using MFCC to obtain a feature dataset, which is then divided into training and testing sets. An LSTM-based substation acoustic signature fault diagnosis model is constructed. The optimal initial parameters are obtained by iteratively optimizing the particle positions using the number of feature points extracted by MFCC, the number of batch samples, the number of network layers, and the number of hidden and fully connected layers. These optimal initial parameters are then input into the LSTM-based substation acoustic signature fault diagnosis model. Finally, the trained LSTM-based substation acoustic signature fault diagnosis model is used to diagnose substation acoustic signature faults. This invention offers good processing efficiency, avoids local optima, and facilitates accurate fault diagnosis results.

This invention discloses a method and apparatus for identifying road changes using remote sensing imagery. The method includes: acquiring new and old remote sensing images of unlabeled roads; cropping the remote sensing images; comparing the cropped road images; inputting the compared road change images into a deep learning model for self-supervised training to obtain a pre-trained model; training the pre-trained model using labeled road remote sensing images to obtain a classification and recognition model; and inputting the desired road change images into the classification and recognition model by transferring the parameters of the self-supervised model to obtain the road change results. The method provided by this invention can intelligently identify road changes by eliminating the need for manual labeling, achieving high efficiency, and reducing costs, thus enabling efficient monitoring and dynamic supervision of road changes. This provides technical and data support for promoting traffic road maintenance and optimization, and implementing road hazard detection and remediation.

This invention discloses an intelligent detection method for product surface anomalies based on machine vision, belonging to the fields of machine vision and anomaly detection technology. The method comprises the following steps: First, image data of the product surface is collected and filtered, comprehensively covering the surface of the product under test and dividing it into regions; the training set consists of normal surface images. Second, a suitable neural network structure is selected, and a loss function is constructed by combining MSE, Cosine, and SSIM; the model parameters are trained and saved. The anomaly heatmap of the verification image is compared with the heatmap of the normal image to determine the optimal threshold. Finally, preprocessing, network computation, and post-processing operations are performed on the test image to determine whether there are anomalies and their locations. This invention uses only surface images of normal products to train the neural network, eliminating the need to pre-collect images of various anomaly surfaces as training data, making it suitable for intelligent detection of various possible defects on the surface of products on assembly lines.

The utility model discloses a quick mounting mechanism for an aero seat ground rail, which relates to the related field of aero seat ground rails, and comprises a bottom plate, and a nut is welded at the lower end of the bottom plate; the fixing sleeve is welded to the upper end of the bottom plate; the clamping blocks are movably arranged at the side opening of the fixing sleeve; the pressing shaft is in sliding fit with the guide hole of the fixing sleeve, and the outer conical surface of the pressing shaft is in contact with the side conical surface of the clamping block; the ground rail is arranged on the periphery of the main body of the fixing sleeve in a sleeving manner; the bolt penetrates through the pressing shaft, the fixing sleeve and the bottom plate and is in threaded connection with the nut; the bolt is screwed down, the bolt drives the pressing shaft to move downwards in the axial direction, the outer conical face pushes the side conical face to enable the clamping block to move outwards in the radial direction, the ground rail is clamped, and therefore the ground rail and the fixing sleeve are connected. The installation process is simple and fast, the installation time is greatly shortened, and the installation efficiency of the aero seat ground rail is effectively improved.

This utility model relates to an automatic cleaning device for large storage tanks, comprising: a base, a base support system, a pole system, a rotating seat, a rotating seat support system, an adjustment system, a power system, and a cleaning system. This device offers advantages such as convenient operation, high automation, strong safety, lightweight design, wide applicability, low operating costs and labor intensity, short construction period, high tank utilization rate, good cleaning effect, flexible cleaning effect acceptance methods, and no risk of operator poisoning. It is suitable for large storage tanks of various sizes and can effectively solve various problems in existing tank cleaning technologies.

This invention discloses a training method and system for quadruped robots based on deep reinforcement learning. The AMP training framework significantly enhances the robot's adaptability to complex environments. By distinguishing the robot's current motion sample distribution from that of a reference sample, the training aims to maximize the probability of misjudgment and the reward for reinforcement learning, enabling the robot to learn motion patterns that meet actual needs. When the environment changes, the robot can adjust its strategy promptly based on new information without remodeling, solving the problem of traditional methods being sensitive to environmental changes. Furthermore, this framework introduces imitation learning into reinforcement learning, providing effective guidance for robot learning, reducing the blindness of random exploration, accelerating the learning speed of effective strategies, and improving training efficiency. Simultaneously, the integrated hierarchical LSTM Actor network can extract and aggregate motion features at multiple time scales, allowing the robot to generate actions that consider both short-term gait and long-term trends, resulting in more natural and fluid movements. It also fully utilizes existing sample information, improving sample utilization and reducing training costs.

The present application belongs to the cross field of biomedical signal processing and artificial intelligence, and particularly relates to a sleep staging system and method based on a double-flow parallel neural network. The system comprises a brain electrical device, a data acquisition and preprocessing module, a HypnoMamba-Dual neural network model and a downstream application module connected in sequence; the brain electrical device is a single-channel electroencephalogram signal acquisition device for acquiring original EEG signals; the data acquisition and preprocessing module is used for filtering and windowing the original EEG signals for standardized operation; the HypnoMamba-Dual neural network model is used for analyzing and calculating the preprocessed EEG signal sequence and outputting a sleep staging sequence. The double-flow architecture of the present application, especially the introduction of the "local reserved flow", provides a mechanism for the model to resist the "context smoothing effect". When the context flow tends to ignore the short N1 period, the strong instantaneous features provided by the local reserved flow can effectively correct the decision, thereby significantly improving the F1 score of the N1 period.