6results about How to "Reliable classification" patented technology

The application discloses an open set cross-network node classification method and device, relates to the field of machine learning, and designs a framework of separating first and then adapting to a domain. First, an unknown class and a known class are separated by constructing a rough boundary through adversarial learning, and then a pseudo label is allocated to iteratively train a model in a self-training manner, so that a more accurate boundary is gradually generated for separating the known class and the unknown class. Secondly, in the domain adaptation stage, a negative domain adaptation coefficient is allocated to the nodes of the unknown class, and a positive domain adaptation coefficient is allocated to the nodes of the known class, so that the nodes of the known class of the target network are aligned with the source network, and the nodes of the unknown class of the target network are pushed away from the source network, thereby realizing the adversarial domain alignment excluding the unknown class, and further realizing the classification of the open set cross-network node with high accuracy.

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 application discloses an open set cross-domain diagnosis method and device based on purity perception orthogonal decoupling network, a medium and a product, and relates to the field of mechanical fault diagnosis.The method comprises the following steps: inputting a sample into a purity-guided orthogonal decoupling architecture to obtain fault semantic features and a sample purity coefficient; taking the sample purity coefficient as prior knowledge, adopting a purity self-adaptive joint optimization strategy to carry out clustering optimization of same-class aggregation and different-class exclusion based on the fault semantic features, training in the purity-guided orthogonal decoupling architecture through an uncertainty perception dynamic joint optimization mechanism, combining an open set identification strategy based on the purity perception dynamic cosine extreme value theory to construct an open set cross-domain diagnosis model, inputting to-be-diagnosed data into the model, and realizing known-class classification and unknown-class rejection of the to-be-diagnosed data.The application can accurately classify known faults and reliably isolate and reject unknown faults under the double challenges of complete invisibility of a target domain and distribution deviation and new class emergence.

The embodiment of the invention discloses a telex actuator driving motor working condition sensing meta learning diagnosis method, which comprises the following steps of: acquiring a feature embedding function value through a working condition encoder module, acquiring a class prototype of a fault class based on a prototype network, and training the prototype network based on prototype loss, establishing center loss and establishing a joint loss function to obtain a fault class model; and based on the trained prototype network, fault diagnosis in the actual working process is carried out. According to the method, high-robustness diagnosis on composite working condition changes under the condition of few samples is realized, the explicit modeling capability on operation conditions is remarkably improved, and the diagnosis precision is greatly improved. According to the method, the discrimination of the fault features in the embedding space is remarkably enhanced. The method has strong small sample rapid adaptive capability, so that when a new fault diagnosis task is encountered, rapid adaptation can be realized without retraining or only a small number of new samples, and the practicability and deployment flexibility of the method are greatly improved.

A monitoring system for a food preparation system includes a sensor unit having at least one sensor for determining current sensor data of food being loaded or unloaded to or from a food processing chamber of the food preparation system; the processing unit is used for determining current feature data according to the current sensor data; the classification unit is used for determining characteristic data of food in the food processing chamber according to the current characteristic data; and a control unit for controlling at least one actuator adapted to notify or alert a user or to set operating parameters of the food preparation system based on the determined characteristic data of the food present within the food processing chamber.