6 results about "Invariant feature extraction" patented technology

This invention discloses a rotation- and scale-invariant feature extraction method and system based on dual-coordinate system collaboration, belonging to the field of image processing technology. Addressing the poor matching performance of existing image feature extraction methods under rotation and scale changes, this invention employs the following scheme: The input image undergoes a logarithmic polar coordinate transformation to generate a polar coordinate image; a dual-branch network is constructed, where the feature extraction branch extracts keypoint location information in a Cartesian coordinate system, and the feature description branch extracts a polar coordinate feature map in a logarithmic polar coordinate system; the keypoint locations in the Cartesian coordinate system are mapped to their corresponding positions in the polar coordinate feature map using a coordinate mapping function, and interpolation sampling is performed to generate feature descriptors that integrate precise location information and rotation / scale invariance information; based on the feature descriptors, matching point pairs are calculated, and the spatial transformation relationship between images is determined. This invention achieves highly robust feature extraction under rotation and scale changes while maintaining high real-time processing efficiency.

This disclosure provides a method, device, electronic equipment, and storage medium for HVAC fault diagnosis, relating to the field of fault diagnosis technology. It acquires multi-source time-series operational data of an HVAC system and performs standardized preprocessing. A multi-scale feature extraction network is constructed, containing convolutional branches with different receptive fields set in parallel and achieving adaptive fusion of features from each branch based on an attention mechanism. This network is then optimized using domain adversarial training to align feature distributions on the source and target domain datasets, thereby obtaining a domain-invariant feature extractor. Finally, this domain-invariant feature extractor is combined with a few-sample learning paradigm of metric learning to calculate feature prototypes for each category in the dataset. The fault category is determined based on the distance metric between the fault query data and the feature prototypes. Therefore, this method can solve the problems of poor model generalization and difficulty in accurately diagnosing faults in small-sample scenarios in existing technologies.

The application discloses a single-source domain target recognition generalization method, product, medium and equipment, relates to the field of domain adaptive target recognition, and comprises the following steps: generating a stylized image corresponding to an original image through a style feature space; encoding original image and stylized image features; jointly decoupling the original image and the stylized image features into domain-invariant features and domain-unique features; training a region candidate network using the domain-invariant features, and optimizing the network using orthogonal loss and target recognition loss functions; and inputting a complex unknown weather sea target image into the optimized network to obtain the category and position of the sea target in the complex unknown weather sea target image. The application can overcome the problems of difficulty in extracting domain-invariant features in complex weather data sets, single style generation and data generated being biased towards source domain distribution, and difficulty in method model generalization, improve the generalization ability of the model, and effectively improve the recognition ability of sea targets under different complex unknown weather conditions.

This invention provides a cross-device domain adaptation method based on domain decoupling and class confusion minimization feature alignment. The main steps include: collecting vibration signals from different devices to construct labeled source domain and unlabeled target domain datasets, and dividing them into training and testing sets; constructing a domain decoupling module based on a feature extractor with convolutional channel separation and a decoder reconstructing the input data, and constructing a classifier and domain discriminator on this basis; constraining the model's learning behavior through sample reconstruction loss, conditional adversarial domain adaptation loss, classification loss, and class confusion minimization loss, the model decouples features into domain-specific features and domain-invariant features to enhance the extraction effect of domain-invariant features, aligns domain-invariant features, and simultaneously minimizes inter-class confusion in the target domain; completing model training on the training set, and finally establishing a high-precision fault diagnosis model to achieve fault diagnosis of the target device. This invention enhances the representation performance of domain-invariant features through the decoupling module, improves feature alignment by combining conditional domain adaptation loss and class confusion minimization loss, and optimizes the classifier's classification behavior in the target domain, effectively addressing the problem of completely missing labels in the target device dataset.

This invention relates to the field of medical equipment management technology and discloses an intelligent surgical instrument identification and real-time inventory image analysis system. The system includes an image acquisition module, an image preprocessing module, a topological feature extraction module, an instrument identification module, a spatiotemporal trajectory analysis module, a state prediction module, and a decision support module. This system achieves high-precision identification of surgical instruments through topological invariant feature extraction technology; accurately identifies instrument usage anomalies using spatiotemporal topological manifold trajectory analysis; and achieves forward-looking prediction of instrument states based on topological manifold learning prediction technology. This overcomes the bottleneck of low accuracy in complex environments inherent in traditional image recognition methods, providing a new technical guarantee for surgical safety.