5results about How to "Comprehensive feature extraction" patented technology

The application discloses a non-intrusive load sensing method fusing multi-time scale electrical appliance features. The method first carries out power consumption data collection and preprocessing, then detects whether an electrical appliance event occurs and whether a power sequence fluctuates. After the event ends and it is found that the power sequence is fluctuating, multi-time scale electrical appliance features are extracted, a sensing model is constructed and trained, and finally, the load sensing fusing the multi-time scale electrical appliance features is carried out. The application can comprehensively fuse the electrical appliance features on the multi-time scale, extract more comprehensive electrical appliance characteristics on different time scales, effectively balance the classification result errors on different time scales, and finally scientifically and reasonably perform the non-intrusive load sensing on the multi-time scale.

This invention discloses an imbalanced malware detection enhancement method based on the fusion of CWGAN-GP data augmentation and TEXTCNN–TRANSFORMER, comprising the following steps: S1, data acquisition and preprocessing: running an executable file in a controlled sandbox environment to dynamically capture its API call sequence, standardizing the API call sequence, and mapping it into a dense vector sequence; S2, data augmentation based on Conditional Wasserstein Generative Adversarial Network (CWGAN-GP) with gradient penalty mechanism: constructing a CWGAN-GP model conditioned on the category labels of minority malware classes, the model including a conditional generator G and a discriminator D with gradient penalty; inputting the minority malware samples obtained in step S1 and their corresponding category labels into the CWGAN-GP model for adversarial training until the model converges. The advantages of this invention are: high-quality data augmentation with semantic fidelity; comprehensive and complementary feature extraction; significant end-to-end performance improvement, especially in minority class identification; and strong model robustness and generalization ability.

The invention discloses a radial tire defect detection method, device and equipment, a medium and a product, and relates to the technical field of tire detection, and the method comprises the following steps: obtaining an X-ray image of a radial tire to be detected; inputting the X-ray image into a tire defect detection model to obtain a defect detection result; the defect detection result comprises a defect type and a defect position; the tire defect detection model is obtained by training a target neural network by adopting a training set; the target neural network is a neural network formed by combining a YOLOv8 network and a RepViT block; and displaying the defect detection result in real time through an image user interface. The tire defect detection efficiency can be improved.

The invention discloses a device and method for monitoring the blocking state of an urban gutter inlet. The device comprises a data processing unit, a waterproof microphone, a humidity sensor, a liquid level sensor, an LED state indicator lamp, a protective shell and a data transmission interface, and the data processing unit is connected with the humidity sensor, the waterproof microphone and the liquid level sensor. The method comprises the steps of training a blockage judgment model of sound features, installing and setting a monitoring device, judging and processing blockage, early warning accumulated water, resetting dormancy and the like. According to the invention, the defects of low labor efficiency, high misjudgment of a single sensor and large interference of light environment on vision in the existing gutter inlet blockage monitoring are overcome.

This invention discloses a robot grasping prediction method based on attention mechanism and low-rank tensor fusion. The method includes: acquiring a dataset of grasping images of a robot grasping multiple different objects; preprocessing the grasping image dataset and dividing it into a training set and a test set; training and testing a model based on attention mechanism and low-rank tensor fusion using the training set and the test set to obtain a trained model; acquiring tactile images of the target object grasped by the robot; inputting the tactile images into the trained model; and outputting a prediction result of whether the target object was successfully grasped. This invention enables the model to better capture key features and important information, achieves more comprehensive and efficient feature extraction by fusing multimodal information, generates richer fused features, and effectively predicts whether grasping is successful.